Structural dynamics problem optimization method and device based on physical planning and enhanced collaborative optimization

Abstract

The invention discloses a structural dynamics problem optimization method and device based on physical planning and enhanced collaborative optimization. The method specifically comprises the following steps: firstly, determining an optimization objective of an optimization problem, dividing subsystems, determining optimization objectives of the subsystems, constructing an ECO model of the optimization problem, and constructing a constraint violation model CVM; then constructing a preference-containing subsystem total objective function, generating a preference function of each interval, and comprehensively generating a preference-containing total objective function; a subsystem layer optimization model is established, a system-level optimization model is constructed and solved, whether the compatibility between the system-level shared design variables and the subsystem design variables meets the convergence precision requirement or not is verified, if yes, optimization is stopped, and if not, the comprehensive penalty coefficient and the design parameters are updated, and optimization is repeated; finally, whether the final optimization result meets the set preference or not is checked, and the optimization result is output. According to the method, the parameter quantity of parameter adjustment is reduced, the optimization result gives consideration to improvement of a plurality of design targets, and common optimization of comprehensive performance is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of multidisciplinary and multi-objective optimization design of structural dynamics, and in particular to a method and device for optimizing structural dynamics problems based on physical planning and enhanced collaborative optimization. Background Art

[0002] In the multidisciplinary and multi-objective optimization problem of complex structural dynamics, multi-objective balance is a very challenging problem. At present, the main methods for achieving multi-objective optimization in the field of multidisciplinary design optimization include weighted sum method, Pareto solution set, game theory, physical programming method, etc. For example, Tappeta et al. used the weighted sum method to solve the multi-objective collaborative optimization (MOCO) problem, expressing the system-level objective function as the weighted sum of the subsystem-level objective functions. Although this method is simple, its effectiveness is highly dependent on the precise setting of the weight coefficient, which is only applicable to convex Pareto space, and the process of determining the normalization coefficient is extremely time-consuming. In addition, although the Pareto solution set method can show rich Pareto frontier results, it is limited by the compatibility of the system-level and subsystem-level Pareto solution sets in practical applications, and usually requires additional decision support tools to make decisions among many Pareto solutions; at the same time, since the Pareto optimal solution of multi-objective optimization contains countless non-dominated solutions, the decision results inevitably have a certain degree of arbitrariness. Game theory methods optimize the Pareto solution set by considering the strategies and preferences of all parties. Although it is very effective in low-dimensional problems, when faced with high-dimensional problems, finding the Pareto frontier still requires a lot of computing resources.

[0003] In summary, on the one hand, in the existing multidisciplinary design optimization of structural dynamics, the solution strategy of the multi-objective optimization design method has poor convergence and often requires a lot of parameter adjustments to achieve basic convergence; on the other hand, there are conflicts between the design objectives of the structural dynamics subsystems, which makes it difficult for the optimization solution strategy to find a feasible solution that meets the designer's preferences. Summary of the invention

[0004] The object of the present invention is to provide a method and device for optimizing structural dynamics problems based on physical planning and enhanced collaborative optimization, which can take into account the improvement of multiple performance indicators at the same time and achieve joint optimization of comprehensive performance.

[0005] The technical solution to achieve the purpose of the present invention is: a structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization, comprising the following steps:

[0006] Step 1: According to the multidisciplinary and multi-objective optimization problem of structural dynamics, determine the optimization target of the optimization problem, divide the subsystem, and determine the optimization target of the subsystem;

[0007] Step 2: Determine the shared design variables and objective functions at the system level, determine the objective functions, constraints and local design variables of the subsystems, and construct an ECO model for the optimization problem;

[0008] Step 3: Initialize the design variables. In the first round of optimization, the calculation is performed with the given initial design values. In the subsequent optimization process, the design variables of the previous round of optimization are used as the starting point for calculation.

[0009] Step 4: Construct the constraint violation model CVM;

[0010] Step 5: Determine the optimization type. If the PP-ECO method is used, proceed to step 6; if the IECO-PP method is used, proceed to step 7;

[0011] Step 6: Construct the subsystem total objective function with preference in the form of PP-ECO, and go to step 8;

[0012] Step 7: Construct the subsystem total objective function with preference in the form of IECO-PP;

[0013] Step 8: According to the existing engineering experience or the optimization results of the single objective function, the preference type and preference interval of each objective function are determined, the preference function of each interval is generated, and the preference functions of each interval are integrated to generate a total objective function containing preferences;

[0014] Step 9: Use the objective function containing the preference as the subsystem optimization objective function of the enhanced collaborative optimization strategy, and establish a subsystem level optimization model;

[0015] Step 10, construct a system-level optimization model and solve it to verify whether the compatibility between the system-level shared design variables and the subsystem design variables meets the convergence accuracy requirements. If so, stop the optimization to obtain a non-inferior solution and proceed to step 12, otherwise, proceed to step 11;

[0016] Step 11, update the comprehensive penalty coefficient and design parameters, and repeat steps 4 to 10 until all subsystem design variables converge;

[0017] Step 12: Check whether the final optimization result meets the set preferences. If so, output the optimization solution and optimization result. Otherwise, jump to step 6 or step 7 to reset the preference type and preference range.

[0018] A structural dynamics problem optimization device based on physical planning and enhanced collaborative optimization, the device is used to implement the structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization, the device includes an initialization module, a CVM optimization solution module, a physical planning module, a subsystem level optimization solution module, a system level optimization solution module, a convergence judgment module and an output processing module;

[0019] The initialization module is configured to initialize parameters for the user, and drives the following units to perform the following initialization and subsequent parameter update steps: divide the subsystem unit to divide the subsystem optimization problem; set the convergence accuracy unit to set the specified accuracy required for system-level optimization; set the optimization parameter unit to set the consistency penalty coefficient λ in the PP-ECO method Ci and the slack variable coefficient λ Fi Parameter or comprehensive penalty coefficient λ in IECO-PP i ; Set the optimization starting point unit, set the starting point of each subsequent optimization system; Penalty coefficient update unit, update the comprehensive penalty coefficient λ in the IECO-PP method i ;

[0020] The CVM optimization solution module is used to configure the solver parameters initially set by the user and drive the following units to perform the optimization solution steps: a CVM objective function setting unit, which defines the objective function in the CVM optimization; a CVM constraint condition setting unit, which sets the constraint conditions in the CVM optimization solution; a CVM solver setting unit, which configures the solver parameters for solving the CVM optimization problem; a CVM communication unit, which realizes the communication between the CVM optimization solution module and other optimization modules and transmits data and information;

[0021] The physical planning module drives the following units to perform the PP step: an objective function setting unit, which sets the objective function in the PP; a preference interval setting unit, which sets the interval of the user's objective function preference value; an objective function preference function generation unit, which generates a preference function of the objective function; and a comprehensive preference function generation unit, which generates a comprehensive preference function.

[0022] The subsystem-level optimization solution module drives the following units to execute the subsystem optimization steps: a subsystem solver setting unit, which configures the solver parameters for subsystem optimization solution; a subsystem objective function setting unit, which sets the objective function of the subsystem; a subsystem constraint condition setting unit, which sets the constraint conditions of the subsystem; a subsystem communication unit, which realizes the communication between the subsystem and other optimization modules and transmits data and information;

[0023] The system-level optimization solution module drives the following units to perform the system-level optimization steps: a system-level objective function setting unit, which sets the system-level objective function; a system-level constraint condition setting unit, which sets the system-level constraint condition; a system-level solver setting unit, which configures the solver parameters for system-level optimization solution; a system-level communication unit, which realizes the communication between the system-level optimization solution module and other modules;

[0024] The convergence judgment module drives the following units to judge whether the optimization process has reached the convergence standard: the judgment unit judges the current optimization process to determine whether the preset convergence standard has been reached; the feedback unit feeds back the judgment result to the initialization module to adjust parameters or terminate the optimization process as needed;

[0025] The output processing module drives the following units to perform processing and output optimization results: the result processing unit processes the optimization solution results; the result output unit outputs the final optimization result;

[0026] The above modules are composed of at least one processor, at least one user interface or output interface, a memory and at least one communication bus; the communication bus is used to realize the connection and communication between the modules; the user interface includes a display, a keyboard or a pointing device; the memory includes a high-speed RAM or a non-volatile memory;

[0027] Operating system, which includes various system programs used to implement various basic services and handle hardware-based tasks;

[0028] The application includes various application programs for realizing various application businesses, including an application program for a structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization, and the application program runs under an operating system; the memory also includes a cache unit, and the cache unit is used to store cache data and optimization results generated during the optimization process.

[0029] Compared with the prior art, the present invention has the following significant advantages: (1) Compared with the traditional weighted ECO method, the present invention reduces a large number of parameter adjustments, and stable convergence can be achieved by simple parameter adjustments; (2) In the multi-objective and multi-disciplinary optimization of structural dynamics, various design objective conflicts can be quickly coordinated by simply setting the starting point of the design variables and the preference information of the actual physical quantities. The optimization results take into account the improvement of multiple design objectives at the same time, and directly obtain the design objectives that meet the engineering satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to the present invention.

[0031] Figure 2 It is a schematic diagram of the structure of the enhanced collaborative optimization solution framework in the present invention.

[0032] Figure 3 It is a schematic diagram of the solution process of the physical planning preference function in the present invention.

[0033] Figure 4 It is a schematic diagram of the integrated calculation process of the total preference function of physical planning in the present invention.

[0034] Figure 5 Graphs showing convergence curves obtained using different methods in embodiments of the present invention.

[0035] Figure 6 Schematic diagram of the gear reducer model and design parameters in an embodiment of the present invention.

[0036] Figure 7 It is a structural block diagram of a structural dynamics problem optimization device based on physical planning and enhanced collaborative optimization in an embodiment of the present invention.

[0037] Figure 8 4 is a structural block diagram of an initialization module in an embodiment of the present invention.

[0038] Fig. 9 It is a structural block diagram of the CVM optimization solution module in an embodiment of the present invention.

[0039] Fig.10 It is a structural block diagram of the physical planning module in an embodiment of the present invention.

[0040] Fig.11 It is a structural block diagram of a subsystem-level optimization solution module in an embodiment of the present invention.

[0041] Fig.12 It is a structural block diagram of a system-level optimization solution module in an embodiment of the present invention.

[0042] Fig.13 4 is a structural block diagram of a convergence judgment module in an embodiment of the present invention.

[0043] Fig.14 4 is a structural block diagram of an output processing module in an embodiment of the present invention.

[0044] Fig.15 4 is a structural block diagram of a module hardware device in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] Aiming at multi-objective and multi-disciplinary optimization problems, the present invention proposes an integrated optimization method of physical programming and enhanced collaborative optimization. By integrating physical programming (PP) with enhanced collaborative optimization (ECO) strategies, an objective function with target preference and its optimization model are constructed in each subsystem, coordinating various conflicts and constraints in system design, while taking into account the improvement of multiple performance indicators, and achieving joint optimization of comprehensive performance.

[0046] Combination Figure 1 , Figure 2The present invention provides a structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization, comprising the following steps:

[0047] Step 1: According to the multidisciplinary and multi-objective optimization problem of structural dynamics, determine the optimization target of the optimization problem, divide the subsystem, and determine the optimization target of the subsystem;

[0048] Step 2: Determine the shared design variables and objective functions at the system level, determine the objective functions, constraints and local design variables of the subsystems, and construct an ECO model for the optimization problem;

[0049] Step 3: Initialize the design variables. In the first round of optimization, the calculation is performed with the given initial design values. In the subsequent optimization process, the design variables of the previous round of optimization are used as the starting point for calculation.

[0050] Step 4: Construct the constraint violation model CVM;

[0051] Step 5: Determine the optimization type. If the PP-ECO method is used, proceed to step 6; if the IECO-PP method is used, proceed to step 7;

[0052] Step 6: Construct the subsystem total objective function with preference in the form of PP-ECO, and go to step 8;

[0053] Step 7: Construct the subsystem total objective function with preference in the form of IECO-PP;

[0054] Step 8: According to the existing engineering experience or the optimization results of the single objective function, the preference type and preference interval of each objective function are determined, the preference function of each interval is generated, and the preference functions of each interval are integrated to generate a total objective function containing preferences;

[0055] Step 9: Use the objective function containing the preference as the subsystem optimization objective function of the enhanced collaborative optimization strategy, and establish a subsystem level optimization model;

[0056] Step 10, construct a system-level optimization model and solve it to verify whether the compatibility between the system-level shared design variables and the subsystem design variables meets the convergence accuracy requirements. If so, stop the optimization to obtain a non-inferior solution and proceed to step 12, otherwise, proceed to step 11;

[0057] Step 11, update the comprehensive penalty coefficient and design parameters, and repeat steps 4 to 10 until all subsystem design variables converge;

[0058] Step 12: Check whether the final optimization result meets the set preferences. If so, output the optimization solution and optimization result. Otherwise, jump to step 6 or step 7 to reset the preference type and preference range.

[0059] As a specific example, in step 1, according to the multidisciplinary and multi-objective optimization problem of structural dynamics, the optimization goal of the optimization problem is determined, the subsystem is divided, and the optimization goal of the subsystem is determined, as follows:

[0060] According to the description of multidisciplinary and multi-objective optimization problem of structural dynamics, the n optimization objectives of the optimization problem are determined by the performance indicators in the project; the structural dynamics optimization problem is divided into subsystem optimization problems, and the number of subsystems i th ≥2, there are m optimization objectives in each subsystem, 1≤m<n.

[0061] As a specific example, in step 2, the shared design variables and objective functions at the system level are determined, the objective functions, constraints and local design variables of the subsystems are determined, and the ECO model of the optimization problem is constructed, as follows:

[0062] Step 2.1: Determine the system-level shared design variable z and its objective function, and determine the objective function of each subsystem

[0063] Constraint g i (x i ,x Li ) and local design variables x Li ;

[0064] Step 2.2: Construct a system-level ECO model. The system-level ECO model is an unconstrained optimization problem that only solves the system-level shared design variables z and the subsystem design variables x. i The consistency problem between them, the system-level ECO model is:

[0065] Find z

[0066]

[0067] No Constriants

[0068] In the formula, i represents the number of subsystems;

[0069] Step 2.3: Construct a subsystem-level ECO model. The objective function of the subsystem-level ECO model includes the global target F0, the inconsistency information model ) and a set of slack variables λ F ∑s; The constraint function of the subsystem includes the local constraint function g of the subsystem i Linearized Models with Nonlocal Constraints

[0070] i-th th The ECO model of each subsystem is:

[0071] Find x=[x i x Li s j ]

[0072]

[0073] stg i (x i ,x Li )≤0

[0074]

[0075] s j ≤0,j={1,…,n},j≠i

[0076] In the formula, is the quadratic model of the global objective, x i is the shared design variable in the ith subsystem, x Li is the local design variable in the ith subsystem, is a copy of the shared variable provided at the system level, g i (x i ,x Li ) is the local constraint function of the subsystem, The linearized model of the non-local constraints is LMNS; j is the constraint function used in the jth subsystem The vector of slack variables, λ Ci is the consistency penalty coefficient, λ Fi is the slack variable coefficient, n s is the number of LMNS;

[0077] Linearized Models with Nonlocal Constraints The calculation formula is:

[0078]

[0079] in, are the constraint model coefficients, which are obtained by the central difference method or by reading the constraint model coefficients when calculating the partial derivative information of the actual data points.

[0080] As a specific example, in step 4, the constraint violation model CVM is constructed as follows:

[0081] In order to solve the problem of local variables in the subsystem level, the constraint functions of each subsystem Contains parameters Therefore, the constraint violation model CVM is required to provide local parameters a copy of;

[0082] The constraint violation model CVM is:

[0083] Find x=[x Li s j ]

[0084]

[0085] stg i (x Li ,s j )≤0

[0086] s i ≤0

[0087] Where n s is the constraint g in the ith subsystem i (x Li ,s j )

[0088] As a specific example, in step 6, the overall objective function of the subsystem with preferences in the form of PP-ECO is constructed as follows:

[0089] For the objective function containing multiple performance indicators in the subsystem, it is integrated with the multi-objective PP method in the ECO subsystem, and the preference type and preference interval are set for each objective function to construct the subsystem total objective function containing preferences;

[0090] The total objective function of the subsystem in the form of PP-ECO with preference is:

[0091]

[0092] As a specific example, in step 7, the total objective function of the subsystem with preference in the form of IECO-PP is constructed as follows:

[0093] Based on step 6, cancel the original consistency penalty coefficient λ Ci and the slack variable coefficient λ Fi , introduce the comprehensive penalty coefficient λ i Model for inconsistency information of design variables And the slack variable ∑s is used as a penalty, and the preference type and preference interval are set for this item to construct a subsystem comprehensive objective function with preference; set λ i The initial value is 0, the preference interval is set between 0 and 0.1, and the subsystem comprehensive objective function containing preference and comprehensive penalty coefficient based on the IECO-PP method is:

[0094]

[0095] As a specific example, in step 8, based on existing engineering experience or single objective function optimization results, the preference type and preference interval of each objective function are determined, the preference function of each interval is generated, and the preference function of each interval is integrated to generate a total objective function containing preferences, combined with Figure 3 , Figure 4 , as follows:

[0096] Step 8.1: Give 5 preference values ​​g based on engineering experience or single-objective optimization results i , i = 1, 2, ..., 5, and set g at g1 p Value, denoted as

[0097] Step 8.2: Find g at the boundaries of each region pi Difference Δy i ,in

[0098]

[0099] Where n sc is the number of soft preferences; β is the convexity parameter;

[0100] Step 8.3: Solve for g at the boundaries of each region p Value g pi ,in

[0101] Step 8.4, find the Δx value of each partition, where Δx1 has no meaning and can be assigned any value, and Δx1 = g i -g i-1 , i=2,3,4,5;

[0102] Step 8.5, find the intercept J of each dividing point, where J1 is meaningless and can be assigned any value, and J i =Δy i / Δx i , i=2,3,4,5;

[0103] Step 8.6, calculate the slope S at each dividing point, where S1 = αJ2, S i =S min , i = 2, 3, 4, 5, where

[0104]

[0105] Step 8.7, get the value and slope of each boundary point and insert them into the following formula to obtain the curve parameters a, b, c, d of area 2 to 5:

[0106]

[0107] Step 8.8: Verify whether a and b are positive, α≤0.1. The smaller the a value is, the larger the range of slope values ​​that can be selected for the boundary is. β is the convexity parameter, b>1.0, and 1.5 is generally taken first. If a and b cannot meet the requirement of being positive, then increase β by 0.5 and continue trying.

[0108] Step 8.9, after confirming a, b, c, d, the preference curve function of zones 2 to 5 can be obtained, and the curve function of zone 1 can be determined;

[0109] In the i-th zone, where i = 2, 3, 4, 5, let:

[0110]

[0111] In the formula, is the scaled position in the ith interval, Δx i is the size of the ith interval, then the curve function of zones 2 to 5 is:

[0112]

[0113] Step 8.10, curve function g in zone 1 P for:

[0114]

[0115] Step 8.11: Integrate the curve functions of zones 1 to 5 to obtain the curve function with preference for the performance index. for:

[0116]

[0117] Step 8.12: Generate the total objective function with preference Applying these response values ​​to their respective preference functions, we can obtain the corresponding preference function g Pj , and then the comprehensive preference function of the physical planning method is obtained, that is, the total objective function containing preference When faced with multidisciplinary design optimization, when the objective function contains physical performance indicators in the subsystem, It is called the comprehensive preference function of the subsystem:

[0118]

[0119] In the formula, g Pj is the preference function of the jth objective, j = 1, 2, ..., m is the number of objective functions; n sc is the number of soft preferences, i.e. n sc =m;

[0120] Step 8.13: For the total objective function with preference Optimize and solve: After that, It is used as the objective function of the optimization problem to solve, and the optimized design variables are inserted into the original objective function to determine whether the performance indicators meet the preferences. If they meet the preferences, it exits and obtains a solution that meets the preference requirements. If not, it returns to step 8.1 to redesign the preference information.

[0121] As a specific example, in step 9, the objective function containing the preference is used as the subsystem optimization objective function of the enhanced collaborative optimization strategy, and a subsystem level optimization model is established, as follows:

[0122] The multi-objective optimization model of the i-th subsystem in ECO form is:

[0123] Find x=[x i x Li s j ]

[0124]

[0125] stg i (x i ,x Li )≤0

[0126]

[0127] s j ≤0,j={1,…,n},j≠i

[0128] The PP-ECO optimization model at the i-th subsystem level is:

[0129] Find x=[x i x Li s j ]

[0130]

[0131] stg i (x i ,x Li )≤0

[0132]

[0133] s j ≤0,j={1,…,n},j≠i

[0134] The IECO-PP optimization model of the i-th subsystem is:

[0135] Find x=[x i x Li s j]

[0136]

[0137] stg i (x i ,x Li )≤0

[0138]

[0139] s j ≤0,j={1,…,n},j≠i

[0140] As a specific example, in step 11, the comprehensive penalty coefficient and the design parameters are updated, and steps 4 to 10 are repeated until the design variables of each subsystem are converged;

[0141] If the PP-ECO method is used, jump to step 3 to update the design parameters; if the IECO-PP method is used, jump to step 3 to update the design parameters and update the comprehensive penalty coefficient λ i , the formula is:

[0142]

[0143] In the formula, γ i is the incremental factor of the comprehensive penalty coefficient, γ i The value is between 0 and 100, i represents the i-th subsystem, and n is the number of iterations. Figure 5 Graphs showing convergence curves obtained using different methods in embodiments of the present invention.

[0144] Combination Figure 7 , the present invention also provides a structural dynamics problem optimization device based on physical planning and enhanced collaborative optimization, including an initialization module, a CVM optimization solution module, a physical planning module, a subsystem-level optimization solution module, a system-level optimization solution module, a convergence judgment module and an output processing module;

[0145] Combination Figure 8 The initialization module is used to configure the user initialization parameters and drive the following units to perform the following initialization and subsequent parameter update steps: divide the subsystem unit to divide the subsystem optimization problem; set the convergence accuracy unit to set the specified accuracy required for system-level optimization; set the optimization parameter unit to set the consistency penalty coefficient λ in the PP-ECO method Ci and the slack variable coefficient λ Fi Parameter or comprehensive penalty coefficient λ in IECO-PP i ; Set the optimization starting point unit, set the starting point of each subsequent optimization system; Penalty coefficient update unit, update the comprehensive penalty coefficient λ in the IECO-PP methodi ;

[0146] Combination Fig. 9 The CVM optimization solution module is used to configure the solver parameters initially set by the user and drive the following units to perform the optimization solution steps: a CVM objective function setting unit, which defines the objective function in the CVM optimization; a CVM constraint condition setting unit, which sets the constraint conditions in the CVM optimization solution; a CVM solver setting unit, which configures the solver parameters for solving the CVM optimization problem; a CVM communication unit, which realizes the communication between the CVM optimization solution module and other optimization modules and transmits data and information;

[0147] Combination Fig.10 , the physical planning module drives the following units to perform the PP step: an objective function setting unit, which sets the objective function in the PP; a preference interval setting unit, which sets the interval of the user's objective function preference value; an objective function preference function generation unit, which generates a preference function of the objective function; a comprehensive preference function generation unit, which generates a comprehensive preference function;

[0148] Combination Fig.11 , the subsystem-level optimization solution module drives the following units to perform subsystem optimization steps: a subsystem solver setting unit, which configures solver parameters for subsystem optimization solution; a subsystem objective function setting unit, which sets the objective function of the subsystem; a subsystem constraint condition setting unit, which sets the constraint conditions of the subsystem; a subsystem communication unit, which realizes communication between the subsystem and other optimization modules and transmits data and information;

[0149] Combination Fig.12 , the system-level optimization solution module drives the following units to perform system-level optimization steps: a system-level objective function setting unit, which sets the system-level objective function; a system-level constraint condition setting unit, which sets the system-level constraint condition; a system-level solver setting unit, which configures the solver parameters for system-level optimization solution; a system-level communication unit, which realizes the communication between the system-level optimization solution module and other modules;

[0150] Combination Fig.13 The convergence judgment module drives the following units to judge whether the optimization process has reached the convergence standard: the judgment unit judges the current optimization process to determine whether the preset convergence standard has been reached; the feedback unit feeds back the judgment result to the initialization module to adjust the parameters or terminate the optimization process as needed;

[0151] Combination Fig.14 , the output processing module drives the following units to perform processing and output optimization results: result processing unit: processes the results of the optimization solution; result output unit, outputs the final optimization result;

[0152] Combination Fig.15 , the above modules are composed of at least one processor, at least one user interface or output interface, memory and at least one communication bus; the communication bus is used to realize the connection and communication between the modules; the user interface includes a display, a keyboard or a clicking device; the memory includes a high-speed RAM or a non-volatile memory; in some implementations, the processor may include an initialization module, wherein the initialization module can set initialization parameters and subsequent parameter update rules through a user interface; the processor may also include an optimization solution module, which solves the optimization model by calling a calculator and a solver program in the memory; the processor may also include an output module, wherein the output module outputs the solution result through the output interface; the memory may store a solver, a calculator and a software program, so that the optimization solution module of the processor can call the solver and the calculator from the memory for optimization solution; the initialization module, the convergence judgment module and the output module can realize their basic business through a software program; in some embodiments, the memory stores an operating system and an application, an executable module or a data structure, or a subset thereof, or an extended set thereof;

[0153] Operating system, which includes various system programs used to implement various basic services and handle hardware-based tasks;

[0154] The application includes various application programs for realizing various application businesses, including an application program for optimizing a structural dynamics problem based on physical planning and enhanced collaborative optimization, and the application program can be run under an operating system; the memory can also include a cache unit, and the cache unit can be used to store cache data and optimization results generated during the optimization process.

[0155] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0156] Example 1 (Calculation process of physical planning and enhanced collaborative optimization integrated method)

[0157] This embodiment introduces the optimization method of structural dynamics problem integrating physical planning and multidisciplinary design optimization of the present invention, which is as follows: in the optimization design process for n performance indicators of structural dynamics, the structural dynamics problem is divided into ω dynamic sub-problems, each dynamic sub-problem has m (1≤m<n) optimization objectives, and the structural dynamics problem includes: system-level optimization problem, optimization problem of each dynamic subsystem, constraint violation model (CVM), etc., through an optimization method integrating physical planning and multidisciplinary design optimization, the system-level optimization problem shares the design variable z, and each dynamic sub-problem contains a common design variable x that affects the optimization objective. i , local design variable x Li, where i represents the i-th subsystem, and the slack variable s in the CVM model j , where j represents the jth subsystem. Optimization design and iteration are performed in sequence to obtain a better solution to the structural dynamics problem. The specific calculation steps are as follows:

[0158] Step 1: Based on the description of the multidisciplinary and multi-objective optimization problem of structural dynamics, determine the n optimization objectives of the optimization problem from the performance indicators in the project; divide the structural dynamics optimization problem into subsystem optimization problems, and the number of subsystems i th ≥2, there are m optimization objectives in each subsystem, 1≤m<n;

[0159] Step 2: Structural dynamics optimization problem Construct the ECO model of the optimization problem. Determine the system-level shared design variable z and its objective function, and determine the objective function of each subsystem Constraint g i (x i ,x Li ), local design variable x Li ; At the same time, the constraint violation model (CVM) and the linearization model of non-local constraints (LMNC) and other information are constructed as needed, including:

[0160] (a) Construct the ECO system level. The ECO system level is an unconstrained optimization problem that only solves the system-level shared design variables z and the subsystem design variables x. i The consistency problem between them, the system-level optimization model is shown in formula (1):

[0161]

[0162] Where: i represents the number of subsystems.

[0163] (b) Construct ECO subsystem level. The objective functions of the subsystem optimization model in the ECO method include: global objective F0, inconsistency information model and a set of slack variables λ F Σs; Secondly, the constraint functions of the subsystem include: subsystem local constraint function g i Linearized Models with Nonlocal Constraints i-th th The subsystem optimization model is shown in formula (1.2):

[0164]

[0165] In the formula, is the quadratic model of the global objective, x i is the shared design variable in the ith subsystem, x Li is the local design variable in the ith subsystem, is a copy of the shared variable provided at the system level, g i (x i ,x Li ) is the local constraint function of the subsystem, is the linearized model of nonlocal constraints (LMNS), which can be further calculated by formula (3); s j is the constraint function used in the jth subsystem The vector of slack variables, λ Ci is the consistency penalty coefficient, λ Fi is the slack variable coefficient, and n s is the number of LMNS.

[0166]

[0167] in, are the constraint model coefficients, which are obtained by the central difference method or by reading the constraint model coefficients when calculating the partial derivative information of the actual data points.

[0168] Step 3: Initialize / update design variables. In the first round of optimization, calculations are performed with the given initial design values. In the subsequent optimization process, the design variables of the previous round of optimization are used as the starting point.

[0169] Step 4: CVM constraint violation model. For the problem of local variables in the subsystem level, that is, in the constraint functions of each subsystem Contains parameters Therefore, a constraint violation model (CVM) is needed to provide local parameters A copy of , builds CVM according to formula (4).

[0170]

[0171] Where: n s is the constraint g in the ith subsystem i (x Li ,s j )

[0172] Step 5: Determine the optimization type. If the PP-ECO method is used, proceed to step 6; if the IECO-PP method is used, proceed to step 7.

[0173] Step 6: Construct the subsystem total objective function with preference in the form of PP-ECO. For the objective function with multiple performance indicators in the subsystem, integrate it with the multi-objective PP method in the ECO subsystem, set the preference type and preference interval for each objective function, and construct the subsystem total objective function with preference. The multi-objective optimization model of the i-th subsystem in the ECO form is shown in formula (5):

[0174]

[0175] Step 7: Based on step 6, construct the subsystem total objective function with preference in the form of IECO-PP, and cancel the original consistency penalty coefficient λ Ci and the slack variable coefficient λ Fi In order to ensure the convergence of the ECO model, the comprehensive penalty coefficient λ is introduced i Model for inconsistency information of design variables As well as the slack variable Σs part, the preference type and preference interval are also set for this item to construct the subsystem comprehensive objective function with preference; set λ i The initial value is 0, and its preference type is set to the smaller the better type of soft preference, and the preference interval is set between 0 and 0.1. The subsystem comprehensive objective function containing preference and comprehensive penalty coefficient based on the IECO-PP method is shown in formula (6):

[0176]

[0177] Step 8: Generate the total objective function of the subsystem according to the PP method. According to the existing engineering experience or the optimization results of the single objective function, determine the preference type and preference interval of each objective function, initially set and debug the α and β values, and generate the preference function of each interval according to the generation and solution process method of the preference function spline curve function.

[0178] Construct the subsystem total objective function of the PP-ECO form with preference as shown in formula (7):

[0179]

[0180] The IECO-PP optimization model of the i-th subsystem is as follows:

[0181]

[0182] Step 9: Establish and calculate the subsystem optimization model. The objective function with preference established in step 8 is used as the subsystem optimization objective function of the enhanced collaborative optimization strategy and calculated.

[0183] Therefore, the PP-ECO optimization model of the i-th subsystem level is shown in formula (9):

[0184]

[0185] Step 10: Calculate the system-level optimization model and verify the system-level convergence. Construct the system-level optimization model and solve it to verify the system-level shared design variables z and subsystem design variables x. iWhether the compatibility between them meets the convergence accuracy requirement, if so, stop the optimization to obtain a non-inferior solution and go to step 12, otherwise, jump to step 11.

[0186] Step 11: Comprehensive penalty coefficient λ i If the PP-ECO method is used, jump to step 3 to update the design parameters; if the IECO-PP method is used, jump to step 3 to update the design parameters and update the comprehensive penalty coefficient λ according to formula (10) i

[0187]

[0188] In the formula, γ i is the incremental factor of the comprehensive penalty coefficient, γ i The value is between 0 and 100, i represents the i-th subsystem, and n is the number of iterations. Repeat steps 4 to 11 until all sublevels converge.

[0189] It should be noted that if the subsystem does not converge in the PP-ECO method, the consistency penalty coefficient λ in the subsystem objective function needs to be Ci and the slack variable coefficient λ Fi If the subsystem does not converge in the IECO-PP method, the comprehensive penalty coefficient λ in the subsystem objective function needs to be adjusted. i Make adjustments.

[0190] Step 12: Check whether the final optimization result meets the set preferences. If so, output the optimization solution and optimization result. Otherwise, jump to step 6 or step 7 and reset the preference type and preference range.

[0191] Example 2 (Enhanced Collaborative Optimization ECO Method)

[0192] The enhanced collaborative optimization ECO method is as follows:

[0193] Step 1: Construct the ECO system level. The ECO system level is an unconstrained optimization problem that only solves the system-level shared design variables z and subsystem design variables x. i The consistency problem between them, the system-level optimization model is shown in formula (11):

[0194]

[0195] Where: i represents the number of subsystems.

[0196] Step 2: Construct the ECO subsystem level. The objective function of the subsystem optimization model in the ECO method is composed of: global target F0, inconsistency information model and a set of slack variables λF ∑s consists of three parts; secondly, the constraint function of the subsystem includes: the local constraint function of the subsystem g i Linearized Models with Nonlocal Constraints These two parts are composed. th The subsystem optimization model is shown in formula (12):

[0197]

[0198] In the formula, is the quadratic model of the global objective, x i is the shared design variable in the ith subsystem, x Li is the local design variable in the ith subsystem, is a copy of the shared variable provided at the system level, g i (x i ,x Li ) is the local constraint function of the subsystem, is the linearized model of nonlocal constraints (LMNS). LMNS enables the needs of other subsystems to be considered when optimizing a subsystem. LMNS can be further calculated by formula (13); s j is the constraint function used in the jth subsystem The vector of slack variables, λ Ci is the consistency penalty coefficient, λ Fi is the slack variable coefficient, and n s is the number of LMNS.

[0199]

[0200] in, are the constraint model coefficients, which can be obtained by the central difference method or by reading the constraint model coefficients when calculating the partial derivative information of the actual data points.

[0201] Step 3: Construct the CVM constraint violation model. Contains parameters This parameter cannot be obtained directly from the system level, so the constraint violation model (CVM) is needed to provide local parameters The copy of CVM is shown in formula (14):

[0202]

[0203] Where: n s is the constraint g in the ith subsystem i (x Li ,s j )

[0204] Step 4: Optimize and solve each system level of ECO by level. Solving ECO is a multi-round iterative process. sys When the given convergence accuracy is met, the iteration is stopped to obtain a feasible solution that meets the consistency requirements. First, the parameters in each system level in the ECO model are initialized, such as λ Ci and λ Fi , we also need to give the optimization starting point. Next, we first solve CVM to get the local parameters The subsystem level is then solved to obtain the subsystem solution, and the solution is used as the starting point for the next round. Finally, the optimized subsystem solution is passed to the system level for optimization and solution. After the solution is obtained, J is determined. sys Whether the value meets the convergence requirements, if it does, stop the iteration to obtain a feasible solution, if not, proceed to the next round of iteration.

[0205] Embodiment 3 (Physical Planning PP Method)

[0206] The physical planning PP method is as follows:

[0207] Step 1: Give 5 preference values ​​g based on engineering experience or single-objective optimization results i , i = 1, 2, ..., 5, and set g at g1 p Value, denoted as

[0208] Step 2: Find g at the boundaries of each region pi Difference Δy i ,in

[0209]

[0210] Where n sc is the number of soft preferences; β is the convexity parameter.

[0211] Step 3: Solve for g at the boundaries of each region p Value g pi in

[0212] Step 4: Find the Δx value of each partition, where Δx1 has no meaning and can be assigned any value, and Δx1 = g i -g i-1 , i=2,3,4,5.

[0213] Step 5: Find the intercept rate J of each dividing point, where J1 is meaningless and can be assigned any value, and J i =Δy i / Δx i , i=2,3,4,5.

[0214] Step 6: Calculate the slope S at each dividing point, where S1 = αJ2, S i =S min , i = 2, 3, 4, 5, where

[0215]

[0216] Step 7: After obtaining the values ​​and slopes of each boundary point, they can be inserted into equation (17) to obtain the curve parameters a, b, c, and d of regions 2 to 5.

[0217]

[0218] Step 8: After obtaining the curve parameters a, b, c, and d, verify whether a and b are positive. α≤0.1. The smaller the value, the larger the range of slope values ​​that can be selected for the boundary. β is the convexity parameter, which must be greater than 1.0, and is generally set to 1.5. If a and b cannot meet the requirement of being positive, increase β by 0.5 and continue trying.

[0219] Step 9: After finally confirming a, b, c, and d, the preference curve functions of zones 2 to 5 can be obtained, and the curve function of zone 1 can be determined by parameters such as S1.

[0220] In the i-th (i=2, 3, 4, 5) zone, let:

[0221]

[0222] Where: is the scaled position in the ith interval, Δx i is the size of the ith interval. Then the curve function of the ith interval in intervals 2 to 5 is

[0223]

[0224] Step 10: Curve function g in zone 1 P It can be calculated by formula (20):

[0225]

[0226] Step 11: The curve functions of zones 1 to 5 are combined as follows to obtain the curve function with preference of the performance index:

[0227]

[0228] Step 12: Generate the overall objective function with preferences Applying these response values ​​to their respective preference functions, we can obtain the corresponding preference function g Pj , and then the comprehensive preference function of the physical planning method is obtained, that is, the total objective function containing preference When faced with multidisciplinary design optimization, when the objective function contains physical performance indicators in the subsystem, is called the combined preference function of the subsystem:

[0229]

[0230] In the formula, g Pj is the preference function of the jth objective, j = 1, 2, ..., m is the number of objective functions; n sc is the number of soft preferences, i.e. n sc =m.

[0231] Step 13: For the total objective function with preference Optimize and solve. After that, The objective function of the optimization problem is solved, and the optimized design variables are inserted back into the original objective function to determine whether the performance indicators meet the preferences. If they meet the preferences, the solution is exited to obtain a solution that meets the preference requirements. If not, the solution returns to step 1 to redesign the preference information.

[0232] Example 4 (PP implementation process)

[0233] This embodiment uses the classic multi-objective problem Constr-Ex problem to demonstrate the calculation process of the PP method. Its mathematical model is as shown in formula (23):

[0234]

[0235] Step 1: According to the single objective optimization results, the preference values ​​are given as listed in Table 1.1.

[0236] Table 1.1 Preference interval settings

[0237] Design goals <![CDATA[Very much expect g i1 > <![CDATA[Desired g i2 > <![CDATA[Tolerable g i3 > <![CDATA[Do not expect g i4 > <![CDATA[Unacceptable g i5 > <![CDATA[f1]]> 0.3 0.55 0.6 0.65 0.7 <![CDATA[f2]]> 1.0 1.2 1.4 1.6 2.0

[0238] Steps 2 to 8: Construct preference curves for the two targets respectively. The initial α and β are 0.05 and 1.5 respectively. After multiple rounds of iterations, the β value is 3.7. The preference curve parameters are listed in Tables 1.2 and 1.3.

[0239] Table 1.2 Preference curve parameters for zones 2 to 5 of design target f1

[0240] area a b c d expect 71040.00 449920.00 -18.74 -0.13 Tolerable 421504.00 2669525.33 -132.93 -0.55 Not expected 3167752.53 20062432.71 -986.43 -4.13 Unacceptable 3111.90 19708.70 -1221.073 40.42

[0241] Table 1.3 Preference curve parameters for zones 2 to 5 of design target f2

[0242] area a b c d expect 17.34 109.84 -2.34 -0.13 Tolerable 1850.00 11716.66 -33.95 -0.722 Not expected 12279.06 77767.42 -246.27 -4.05 Unacceptable 91523.52 579649.01 -1824.74 -30.44

[0243] Step 9: Get the preference function for the highly desired region. Set its is 0.1, and the S1 value is 1.35; the same setting is made in f2 is 0.1, we get S1 The value is 1.70.

[0244] Steps 10-12: Obtained comprehensive preference function as the objective function in the minimization problem.

[0245] Find x=[x1 x2]

[0246]

[0247] stg1(x)=x2+9x1≥6

[0248] g2(x)=-x2+9x1≥1

[0249] where 0.1≤x1≤1,0≤x2≤5

[0250] Step 13: Taking the starting point as [1,1], the gradient optimizer is used to calculate the design variables as [0.66,0], and the result is [0.66,1.5], which meets the set preference.

[0251] Example 5 (Physical Planning and Enhanced Collaborative Optimization Integrated Method 1 - PP-ECO)

[0252] The PP-ECO method integrates the PP and ECO strategies and constructs an objective function with target preference and its optimization model in each subsystem. The core improvements of the PP-ECO method are as follows:

[0253] Step 1: Construction of ECO-based multi-objective optimization model. Divide the complex multi-objective optimization design problem into subsystems and determine the system-level shared design variables z; then determine the multi-objective functions of each subsystem. Local design variable x Li ; According to the ECO method, the system optimization model, subsystem optimization model and CVM optimization model are constructed. The multi-objective functions of the subsystem include: global multi-objective Inconsistent Information Model and the slack variable λ F ∑s; Constraints include the constraints of the system itself g i (x i ,x Li ) and the linear model with nonlocal constraints (LMNC) The multi-objective optimization model of the i-th subsystem in ECO form is shown in formula (24):

[0254]

[0255] Step 2: Reconstruct the multi-objective of ECO subsystem based on PP method. When facing multi-objective and multi-disciplinary design optimization, the subsystem contains multiple performance indicators. The PP method is used for integration, that is, by setting the preference type and preference interval for each objective function, and the inconsistent information model and the slack variable λ F The two parts of ∑s remain unchanged, and the total objective function of the subsystem in the form of PP-ECO with preference is constructed as shown in formula (25):

[0256]

[0257] According to the existing engineering experience or the optimization results of the single objective function, the preference type and preference interval of each objective function are determined, the α and β values ​​are initially set and debugged, and the preference functions of each interval are generated according to the generation and solution process method of the preference function spline curve function.

[0258] Step 3: Construct the subsystem multi-objective optimization model based on PP-ECO. The subsystem total objective function (25) with preference is used as the subsystem optimization objective function. Its constraints are the original subsystem constraints g i (x i ,x Li ) and the linear model with nonlocal constraints (LMNC) In addition to these two parts, it is also necessary to construct the preference constraints of PP Therefore, the PP-ECO optimization model at the i-th subsystem level is shown in formula (26):

[0259]

[0260] Example 6 (Physical Planning and Enhanced Collaborative Optimization Integrated Method 2 - IECO-PP)

[0261] The proposed IECO-PP method is based on the multi-objective reconstruction of the ECO subsystem based on the PP method in Example 5 (PP-ECO method), and further integrates the inconsistency information and slack variables in the original ECO method into an item containing preference information to form a new subsystem comprehensive objective function. The core improvements of the IECO-PP method are as follows:

[0262] Step 1: Construct a subsystem comprehensive objective function with preference and penalty coefficients. Based on the PP-ECO integration method, the inconsistency information of design variables is further and slack variables ∑s are partially merged into one item and a comprehensive penalty coefficient λ is introducedi To punish its breach of contract, set the preference type and preference interval for this item, and incorporate it into the global multi-objective item with preference, thereby canceling the consistency penalty coefficient λ Ci and the slack variable coefficient λ Fi The subsystem comprehensive objective function containing preference and comprehensive penalty coefficient based on the IECO-PP method is shown in formula (27):

[0263]

[0264] Among them, λ i The initial value is 0, and its preference type is set to the smaller the better type of soft preference, and the preference interval is set between 0 and 0.1. The IECO-PP optimization model of the i-th subsystem is as follows:

[0265]

[0266] Step 2: Comprehensive penalty coefficient λ i To ensure that the system can converge after multiple iterations, the comprehensive penalty coefficient λ can be updated in an exponential growth manner according to the formula i :

[0267]

[0268] In the formula, γ i is the incremental factor of the comprehensive penalty coefficient, γ i The value is between 0 and 100, i represents the i-th subsystem, and n is the number of iterations.

[0269] Example 7 (PP-ECO implementation process)

[0270] This example uses the multidisciplinary and multi-objective gear reducer engineering problem as an example to demonstrate the implementation process of PP-ECO. In the gear reducer model, x1 is the tooth width, in cm; x2 is the gear module; x3 is the number of teeth on the pinion; x4 and x5 are the bearing spacing, in cm; x6 and x7 are the shaft diameters, in cm. Figure 6 shown.

[0271] The gear reducer model needs to realize the minimization of the total volume (or mass) f1(x) of the gear reducer, the stress f2(x) and f3(x) on the first and second axes of the gear reducer, respectively. The optimization mathematical model is as follows:

[0272]

[0273] Due to space and stress limitations, the specific constraints of this model are as follows:

[0274] (1) g1 is the upper limit of the bending stress of the gear teeth; g2 is the upper limit of the contact stress of the gear teeth

[0275]

[0276] (2) g3~g4 are the upper limits of the lateral deflection of the shaft

[0277]

[0278] (3) g5~g6 are the upper limits of the stress of the axis

[0279]

[0280] (4) g7 to g9 are based on spatial or empirical size constraints

[0281]

[0282] (5) g 10 ~g 11 It is the design condition constraint of the shaft determined based on experience

[0283]

[0284] (6) In addition, the design variables also have design boundaries, namely: x1∈[2.6,3.6], x2∈[0.7,0.8], x3∈[17,28], x4∈[7.3,8.3], x5∈[7.3,8.3], x6∈[2.9,3.9], x7∈[5.0,5.5].

[0285] Step 1: Description of multidisciplinary and multi-objective optimization problem. The gear reducer model optimization problem is analyzed, and the optimization objective of the optimization problem is determined as shown in formula (36), and the constraints are shown in formula (37).

[0286]

[0287] Step 2: Construct the ECO model of the optimization problem. First, realize the subsystem division of the multi-objective optimization problem; determine the system-level shared design variable z and its objective function, and determine the objective function of each subsystem. Constraint g i (x i ,x Li ), local design variable x Li ; At the same time, construct CVM, LMNS and other information as needed.

[0288] The optimization model of subsystem 1 is:

[0289]

[0290] The optimization model of subsystem 2 is:

[0291]

[0292] The system-level optimization model of the gear reducer model is:

[0293]

[0294] Step 3: Initialize / update design variables. In the first round of optimization, calculations are performed with the given initial design values. In the subsequent optimization process, the design variables of the previous round of optimization are used as the starting point.

[0295] Step 4: Build CVM. The problem of local variables in the subsystem level is shown in equations (41) and (42).

[0296] The CVM1 optimization model is:

[0297]

[0298] The CVM2 optimization model is:

[0299]

[0300] Step 5: Determine the optimization type, use the PP-ECO method, and proceed to step 6.

[0301] Step 6: For the objective function containing multiple performance indicators in the subsystem, integrate it with the multi-objective PP method in the ECO subsystem, set the preference type and preference interval for each objective function, and construct the subsystem total objective function containing preferences. The multi-objective optimization model (36) and constraints (37) of the gear reducer are reconstructed and decomposed into two subsystem optimization problems with multi-objective linearity.

[0302] The optimization model of ECO-PP subsystem 1 is:

[0303]

[0304] The optimization model of ECO-PP subsystem 2 is:

[0305]

[0306] According to the optimization results of the single objective function, the preference types and preference intervals of each objective function are determined. The settings of the preference intervals of the gear reducer model subsystem 1 and subsystem 2 are shown in Tables 1.4 and 1.5.

[0307] Table 1.4 Preference interval settings for subsystem 1

[0308] Design goals <![CDATA[Very much expect g i1 > <![CDATA[Desired g i2 > <![CDATA[Tolerable g i3 > <![CDATA[Do not expect g i4 > <![CDATA[Unacceptable g i5 > <![CDATA[J s1-1 ]]> 1800 2000 2500 2700 3000 <![CDATA[J s1-2 ]]> 696.137 796.137 896.137 996.137 1100

[0309] Table 1.5 Preference interval settings for subsystem 2

[0310] Design goals <![CDATA[Very much expect g i1 > <![CDATA[Desired g i2 > <![CDATA[Tolerable g i3 > <![CDATA[Do not expect g i4 > <![CDATA[Unacceptable g i5 > <![CDATA[J s2-1 ]]> 1114.66 1164.66 1214.66 1254.66 1300 <![CDATA[J s2-2 ]]> 754.87 778.6525 802.435 826.2175 850

[0311] Initially set and debug α and β values, α value is set to 0.01, β value is initially set to 1.3, and after multiple rounds of iterations, it is set to 1.7. The preference function of each interval is generated according to the generation and solution process of the preference function spline curve function. The preference curve parameters are listed in Table 1.6, Table 1.7, Table 1.8 and Table 1.9.

[0312] Table 1.6 Design target J s1-1 Preference curve parameters for zones 2 to 5

[0313] area a b c d expect <![CDATA[4.46×10 -10 ]]> <![CDATA[2.82×10 -9 ]]> 0.0075 -0.27 Tolerable <![CDATA[9.40×10 -11 ]]> <![CDATA[5.95×10 -10 ]]> 0.033 -1.81 Not expected <![CDATA[6.038×10 -8 ]]> <![CDATA[3.82×10 -7 ]]> 1.05 -35.53 Unacceptable <![CDATA[1.21×10 -7 ]]> <![CDATA[7.69×10 -7 ]]> 8.14 -335.31

[0314] Table 1.7 Design target J s1-2 Preference curve parameters for zones 2 to 5

[0315] area a b c d expect <![CDATA[7.14×10 -9 ]]> <![CDATA[4.52×10 -8 ]]> 0.015 -0.27 Tolerable <![CDATA[7.44×10 -8 ]]> <![CDATA[4.71×10 -7 ]]> 0.17 -2.64 Not expected <![CDATA[8.91×10 -7 ]]> <![CDATA[5.64×10 -6 ]]> 2.08 -31.59 Unacceptable <![CDATA[9.06×10 -6 ]]> <![CDATA[5.74×10 -5 ]]> 23.82 -372.83

[0316] Table 1.8 Design target J s2-1 Preference curve parameters for zones 2 to 5

[0317] area a b c d expect <![CDATA[1.14×10 -7 ]]> <![CDATA[7.23×10 -7 ]]> 0.030 -0.27 Tolerable <![CDATA[1.19×10 -6 ]]> <![CDATA[7.54×10 -6 ]]> 0.34 -2.64 Not expected <![CDATA[3.57×10 -5 ]]> <![CDATA[2.26×10 -4 ]]> 5.22 -32.85 Unacceptable <![CDATA[2.46×10 -4 ]]> <![CDATA[1.55×10 -3 ]]> 54.42 -365.01

[0318] Table 1.9 Design target J s2-2 Preference curve parameters for zones 2 to 5

[0319] area a b c d expect <![CDATA[2.23×10 -6 ]]> <![CDATA[1.41×10 -5 ]]> 0.063 -0.27 Tolerable <![CDATA[2.32×10 -5 ]]> <![CDATA[1.47×10 -4 ]]> 0.73 -2.64 Not expected <![CDATA[2.78×10 -4 ]]> <![CDATA[1.76×10 -3 ]]> 8.75 -31.59 Unacceptable <![CDATA[3.31×10 -3 ]]> 0.02 104.13 375.73

[0320] Step 8: Establish a subsystem optimization model. The objective function with preference established in step 6 is used as the subsystem optimization objective function of the enhanced collaborative optimization strategy and calculated, where λ is set C1 is 0.1, λ F1 is 10, λ C2 is 1000, λ F2 is 10.

[0321] Step 9: Build a system-level optimization model.

[0322] Step 10: This example uses the SLSQP optimizer for optimization, with the starting point x = [2.8, 0.71, 25.0, 7.9, 7.599, 3.0, 5.099], and converges after 16 iterations. In the reducer example, the calculation results using the ECO-PP method are listed in Table 1.10.

[0323] Table 1.10 Reducer multi-objective performance calculation results of the ECO-PP method

[0324] method <![CDATA[J s1-1 ]]> <![CDATA[J s1-2 ]]> <![CDATA[J s2-1 ]]> <![CDATA[J s2-2 ]]> Overall goal ECO-PP 1967.47 817.30 1186.01 808.70 4801.03

[0325] Example 8 (IECO-PP implementation process)

[0326] This embodiment takes the multidisciplinary and multi-objective gear reducer engineering problem as an example to demonstrate the process of implementing IECO-PP.

[0327] Step 1: Description of multidisciplinary and multi-objective optimization problem: Analyze the gear reducer model optimization problem and determine that the optimization objectives and constraints of the optimization problem are consistent with those in Example 7.

[0328] Step 2: Construct the ECO model of the optimization problem. First, realize the subsystem division of the multi-objective optimization problem; determine the system-level shared design variable z and its objective function, and determine the objective function of each subsystem. Constraint g i (x i ,x Li ), local design variable x Li ; At the same time, construct CVM, LMNS and other information as needed.

[0329] Step 3: Initialize / update design variables. In the first round of optimization, calculations are performed with the given initial design values. In the subsequent optimization process, the design variables of the previous round of optimization are used as the starting point.

[0330] Step 4: Construct CVM. The problem of local variables existing in the subsystem level is consistent with that shown in equations (41) and (42) in Example 7.

[0331] Step 5: Determine the optimization type, use the IECO-PP method, and proceed to step 7.

[0332] Step 7: Based on step 6 in Example 7, cancel the original consistency penalty coefficient λ Ci and the slack variable coefficient λ Fi , set λ i The initial value is 0, and the objective function of the subsystem with target preference is constructed based on the ECO strategy and the subsystem level optimization model is established. It is reconstructed and decomposed into two subsystem optimization problems with multi-objective linearity.

[0333] The optimization model of IECO-PP subsystem 1 is:

[0334]

[0335] The optimization model of IECO-PP subsystem 2 is:

[0336]

[0337] The settings of the preference intervals of the gear reducer model subsystem 1 and subsystem 2 are shown in Table 1.4 and Table 1.5 in Example 7. In addition, the subsystem inconsistency information and slack variable item preference intervals are shown in Table 1.11.

[0338] Table 1.11 Subsystem inconsistency information and slack variable item preference settings

[0339] Design goals <![CDATA[Very much expect g i1 > <![CDATA[Desired g i2 > <![CDATA[Tolerable g i3 > <![CDATA[Do not expect g i4 > <![CDATA[Unacceptable g i5 > <![CDATA[J s1c ]]> 0 0.025 0.05 0.075 0.1 <![CDATA[J s2c ]]> 0 0.025 0.05 0.075 0.1

[0340] Initial setting and debugging of α and β values, α value is set to 0.01, β value is initially set to 1.3, and after multiple rounds of iterations is set to 1.7. The preference function of each interval is generated according to the generation and solution process of the preference function spline curve function. The preference curve parameters are listed in Table 1.6, Table 1.7, Table 1.8 and Table 1.9 in Example 7. For the inconsistency information and slack variable item preferences, since their preference interval ranges are set the same, only J is used. s1c For example, as shown in Table 1.12.

[0341] Table 1.12 Design target J s1c Preference curve parameters for zones 2 to 5

[0342] area a b c d expect 1566720.00 9922560.00 51.67 -0.22 Tolerable 13682688.00 86657024.00 511.17 -1.70 Not expected 140635750.40 890693085.86 5221.46 -17.46 Unacceptable 1433984232.10 9081900136.67 53255.51 -177.98

[0343] Step 8: Establish a subsystem optimization model. The objective function with preference established in step 7 is used as the subsystem optimization objective function of the enhanced collaborative optimization strategy and calculated, where γ1 is set to 0.9 and γ2 is set to 1.8.

[0344] Step 9: Constructing a system-level optimization model. The system-level optimization model of the gear reducer model is consistent with that in Example 7.

[0345] Step 10: This example uses the SLSQP optimizer for optimization, with the starting point x = [2.8, 0.71, 25.0, 7.9, 7.599, 3.0, 5.099], and converges after 19 iterations. In the reducer example, the calculation results using the ECO-PP method are listed in Table 1.13.

[0346] Table 1.13 Reducer multi-objective performance calculation results of ECO-PP method

[0347] method <![CDATA[J s1-1 ]]> <![CDATA[J s1-2 ]]> <![CDATA[J s2-1 ]]> <![CDATA[J s2-2 ]]> Overall goal IECO-PP 1923.62 701.45 1215.56 797.12 4637.78

[0348] It can be seen from the results that both ECO-PP and IECO-PP can obtain feasible solutions and meet the set preference requirements. Most of the design objectives are controlled within the tolerable range and the design objectives are relatively balanced.

[0349] Compared with the weighted ECO method, the IECO-PP method and the PP-ECO method reduce the tedious parameter adjustment process. Only by setting the relevant preferences, more robust convergence performance and better performance index results can be obtained. From the convergence process, it can be seen that the convergence times of the IECO-PP method and the PP-ECO method are not much different, but the convergence process of the IECO-PP method is smoother, which also shows that the IECO-PP method is better than the PP-ECO method.

[0350] Example 9 (Implementation Method of Physical Planning and Enhanced Collaborative Optimization Integrated Device)

[0351] This embodiment is a physical planning and enhanced collaborative optimization integrated device, such as Figure 7 As shown, the device embodiment and Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to electronic equipment.

[0352] The physical planning and enhanced collaborative optimization integrated device of this embodiment includes:

[0353] Initialize the module, such as Figure 8 As shown, the configuration is used for user initialization parameters, driving the following units to perform the following initialization and subsequent parameter update steps: divide the subsystem unit to divide the subsystem optimization problem; set the convergence accuracy unit to set the specified accuracy required for system-level optimization; set the optimization parameter unit to set the consistency penalty coefficient λ in the PP-ECO method Ci and the slack variable coefficient λ Fi Parameter or comprehensive penalty coefficient λ in IECO-PP i ; Set the optimization starting point unit, set the starting point of each subsequent optimization system; Penalty coefficient update unit, update the comprehensive penalty coefficient λ in the IECO-PP method i .

[0354] CVM optimization solution module, such as Fig. 9 As shown, it is used to configure the solver parameters initially set by the user and drive the following units to execute the optimization solution steps: CVM objective function setting unit, which defines the objective function in CVM optimization; CVM constraint condition setting unit, which sets the constraint conditions in CVM optimization solution; CVM solver setting unit, which configures the solver parameters for solving CVM optimization problems; CVM communication unit, which realizes the communication between CVM optimization solution module and other optimization modules and transmits data and information.

[0355] Physical planning modules, such as Fig.10As shown, the following units are driven to execute the PP steps: an objective function setting unit, which sets the objective function in the PP; a preference interval setting unit, which sets the interval of the user's objective function preference value; an objective function preference function generating unit, which generates a preference function of the objective function; and a comprehensive preference function generating unit, which generates a comprehensive preference function.

[0356] Subsystem-level optimization solution modules, such as Fig.11 As shown, the following units are driven to execute the subsystem optimization steps: a subsystem solver setting unit, which configures the solver parameters used for subsystem optimization solution; a subsystem objective function setting unit, which sets the objective function of the subsystem; a subsystem constraint condition setting unit, which sets the constraint conditions of the subsystem; and a subsystem communication unit, which realizes the communication between the subsystem and other optimization modules and transmits data and information.

[0357] System-level optimization solution modules, such as Fig.12 As shown, the following units are driven to execute the system-level optimization steps: a system-level objective function setting unit, which sets the system-level objective function; a system-level constraint condition setting unit, which sets the system-level constraint conditions; a system-level solver setting unit, which configures the solver parameters for system-level optimization solving; and a system-level communication unit, which realizes the communication between the system-level optimization solving module and other modules.

[0358] Convergence judgment module, such as Fig.13 As shown, the following units are driven to execute the judgment on whether the optimization process reaches the convergence standard: the judgment unit judges the current optimization process to determine whether the preset convergence standard is reached; the feedback unit feeds back the judgment result to the initialization module to adjust the parameters or terminate the optimization process as needed.

[0359] Output processing modules, such as Fig.14 As shown, the following units are driven to perform processing and output optimization results: result processing unit: processes the results of the optimization solution; result output unit, outputs the final optimization result.

[0360] like Fig.15As shown, the module may include at least one processor, such as a CPU (Central Processing Unit), at least one user interface or output interface, a memory, and at least one communication bus. The communication bus is used to realize the connection and communication between the above components. The user interface may optionally include a display, a keyboard, or a clicking device (e.g., a mouse, a trackball, a touchpad, or a touch display screen). The memory may include a high-speed RAM (Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. In some implementations, the processor may include an initialization module, wherein the initialization module may set initialization parameters and subsequent parameter update rules through a user interface. The processor may also include an optimization solution module, which solves the optimization model by calling a calculator and a solver program in the memory. The processor may also include an output module, wherein the output module outputs the result obtained by the solution through an output interface. The memory may store a solver, a calculator, and a software program, so that the optimization solution module of the processor can call the solver and the calculator from the memory for optimization solution; the initialization module, the convergence judgment module, and the output module can implement their basic services through the software program. In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof:

[0361] The operating system contains various system programs used to implement various basic services and handle hardware-based tasks.

[0362] The application program includes various application programs for implementing various application services. Specifically, the application program may include an application program of the physical planning and enhanced collaborative optimization integrated method of the present application, and the application program may be run under an operating system. Optionally, the memory may also include a cache unit. The cache unit may be used to store cache data and optimization results generated during the optimization process.

[0363] It is worth noting that the modules or units involved in the embodiments of the present application can be implemented by software or by hardware. The modules or units described can also be set in a processor, for example, it can be described as: a processor includes an initialization module, a physical planning module, an optimization solution module, a convergence judgment module and an output module. Among them, the names of these modules do not constitute a limitation on the module itself under certain circumstances. For example, the initialization module can also be described as a "module configured to initialize user parameters and update parameters". As another aspect, the present application also provides a computer-readable storage medium, which can be a computer-readable storage medium included in the device described in the above embodiment; or it can be a computer-readable storage medium that exists alone and is not assembled in a terminal. The computer-readable storage medium stores one or more programs, and the program is used by one or more processors to execute the physical planning and enhanced collaborative optimization integration method described in the present application. The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in this application.

[0364] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An optimization method for structural dynamics problems based on physical planning and enhanced collaborative optimization, It is characterized in that The following steps are involved: Step 1: According to the multidisciplinary and multi-objective optimization problem of structural dynamics, determine the optimization target of the optimization problem, divide the subsystem, and determine the optimization target of the subsystem; Step 2: Determine the shared design variables and objective functions at the system level, determine the objective functions, constraints and local design variables of the subsystems, and construct an ECO model for the optimization problem; Step 3: Initialize the design variables. In the first round of optimization, the calculation is performed with the given initial design values. In the subsequent optimization process, the design variables of the previous round of optimization are used as the starting point for calculation. Step 4: Construct the constraint violation model CVM; Step 5: Determine the optimization type. If the PP-ECO method is used, proceed to step 6; if the IECO-PP method is used, proceed to step 7; Step 6: Construct the subsystem total objective function with preference in the form of PP-ECO, and go to step 8; Step 7: Construct the subsystem total objective function with preference in the form of IECO-PP; Step 8: According to the existing engineering experience or the optimization results of the single objective function, the preference type and preference interval of each objective function are determined, the preference function of each interval is generated, and the preference functions of each interval are integrated to generate a total objective function containing preferences; Step 9: Use the objective function containing the preference as the subsystem optimization objective function of the enhanced collaborative optimization strategy, and establish a subsystem level optimization model; Step 10, construct a system-level optimization model and solve it to verify whether the compatibility between the system-level shared design variables and the subsystem design variables meets the convergence accuracy requirements. If so, stop the optimization to obtain a non-inferior solution and proceed to step 12, otherwise, proceed to step 11; Step 11, update the comprehensive penalty coefficient and design parameters, and repeat steps 4 to 10 until all subsystem design variables converge; Step 12: Check whether the final optimization result meets the set preferences. If so, output the optimization solution and optimization result. Otherwise, jump to step 6 or step 7 to reset the preference type and preference range.

2. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 1, It is characterized in that In step 1, according to the multidisciplinary and multi-objective optimization problem of structural dynamics, the optimization goal of the optimization problem is determined, the subsystem is divided, and the optimization goal of the subsystem is determined, as follows: According to the description of multidisciplinary and multi-objective optimization problem of structural dynamics, n optimization objectives of the optimization problem are determined by the performance indicators in the engineering; The structural dynamics optimization problem is divided into subsystem optimization problems. The number of subsystems i th ≥2, there are m optimization objectives in each subsystem, 1≤m<n.

3. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 2, It is characterized in that In step 2, the shared design variables and objective functions at the system level are determined, the objective functions, constraints and local design variables of the subsystems are determined, and the ECO model of the optimization problem is constructed as follows: Step 2.1: Determine the system-level shared design variable z and its objective function, and determine the objective function of each subsystem Constraint g i (x i ,x Li ) and local design variables x Li ; Step 2.2: Construct a system-level ECO model. The system-level ECO model is an unconstrained optimization problem that only solves the system-level shared design variables z and the subsystem design variables x. i The consistency problem between them, the system-level ECO model is: Find z No Constriants In the formula, i represents the number of subsystems; Step 2.3: Construct a subsystem-level ECO model. The objective function of the subsystem-level ECO model includes the global objective F 0 , Inconsistency Information Model and a set of slack variables λ F ∑s; The constraint function of the subsystem includes the local constraint function g of the subsystem i Linearized Models with Nonlocal Constraints i-th th The ECO model of each subsystem is: Find x=[x i x Li s j ] s.t.g i (x i ,x Li )≤0 s j ≤0,j={1,…,n},j≠i In the formula, is the quadratic model of the global objective, x i is the shared design variable in the ith subsystem, x Li is the local design variable in the ith subsystem, is a copy of the shared variable provided at the system level, g i (x i ,x Li ) is the local constraint function of the subsystem, The linearized model of the nonlocal constraints is LMNS; j is the constraint function used in the jth subsystem The vector of slack variables, λ Ci is the consistency penalty coefficient, λ Fi is the slack variable coefficient, n s is the number of LMNS; Linearized Models with Nonlocal Constraints The calculation formula is: in, are the constraint model coefficients, which are obtained by the central difference method or by reading the constraint model coefficients when calculating the partial derivative information of the actual data points.

4. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 3, It is characterized in that In step 4, the constraint violation model CVM is constructed as follows: In order to solve the problem of local variables in the subsystem level, the constraint functions of each subsystem Contains parameters Therefore, the constraint violation model CVM is required to provide local parameters a copy of; The constraint violation model CVM is: Find x=[x Li s j ] s.t.g i (x Li ,s j )≤0 s i ≤0 Where n s is the constraint g in the ith subsystem i (x Li ,s j ) 5. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 4, It is characterized in that In step 6, the total objective function of the subsystem with preferences in the form of PP-ECO is constructed as follows: For the objective function containing multiple performance indicators in the subsystem, it is integrated with the multi-objective PP method in the ECO subsystem, and the preference type and preference interval are set for each objective function to construct the subsystem total objective function containing preferences; The total objective function of the subsystem in the form of PP-ECO with preference is:

6. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 4, It is characterized in that In step 7, the total objective function of the subsystem with preference in the form of IECO-PP is constructed as follows: Based on step 6, cancel the original consistency penalty coefficient λ Ci and the slack variable coefficient λ Fi , introduce the comprehensive penalty coefficient λ i Model for inconsistency information of design variables And the slack variable ∑s is used as a penalty, and the preference type and preference interval are set for this item to construct a subsystem comprehensive objective function with preference; set λ i The initial value is 0, the preference interval is set between 0 and 0.1, and the subsystem comprehensive objective function containing preference and comprehensive penalty coefficient based on the IECO-PP method is:

7. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 5 or 6, It is characterized in that In step 8, based on existing engineering experience or single objective function optimization results, the preference types and preference intervals of each objective function are determined, the preference functions of each interval are generated, and the preference functions of each interval are integrated to generate a total objective function containing preferences, as follows: Step 8.1: Give 5 preference values ​​g based on engineering experience or single-objective optimization results i , i = 1, 2, ..., 5, and set g 1 g p Value, denoted as Step 8.2: Find g at the boundaries of each region pi Difference Δy i ,in Where n sc is the number of soft preferences; β is the convexity parameter; Step 8.3: Solve for g at the boundaries of each region p Value g pi ,in Step 8.4, calculate the Δx value of each partition, where Δx 1 It has no meaning and can be assigned any value, while Δx 1 =g i -g i-1 , i=2,3,4,5; Step 8.5: Calculate the intercept J at each dividing point, where J 1 It has no meaning and can be assigned any value, while J i =Δy i / Δx i , i=2,3,4,5; Step 8.6: Calculate the slope S at each dividing point, where S 1 =αJ 2 , S i =S min , i = 2, 3, 4, 5, where Step 8.7, get the value and slope of each boundary point and insert them into the following formula to obtain the curve parameters a, b, c, d of area 2 to 5: Step 8.8: Verify whether a and b are positive, α≤0.

1. The smaller the a value is, the larger the range of slope values ​​that can be selected for the boundary is. β is the convexity parameter, b>1.0, and 1.5 is generally taken first. If a and b cannot meet the requirement of being positive, then increase β by 0.5 and continue trying. Step 8.9, after confirming a, b, c, d, the preference curve function of zones 2 to 5 can be obtained, and the curve function of zone 1 can be determined; In the i-th zone, where i = 2, 3, 4, 5, let: In the formula, is the scaled position in the ith interval, Δx i is the size of the ith interval, then the curve function of zones 2 to 5 is: Step 8.10, curve function g in zone 1 P for: Step 8.11: Integrate the curve functions of zones 1 to 5 to obtain the curve function with preference for the performance index. for: Step 8.12: Generate the total objective function with preference Applying these response values ​​to their respective preference functions, we can obtain the corresponding preference function g Pj , and then the comprehensive preference function of the physical planning method is obtained, that is, the total objective function containing preference When faced with multidisciplinary design optimization, when the objective function contains physical performance indicators in the subsystem, It is called the comprehensive preference function of the subsystem: In the formula, g Pj is the preference function of the jth objective, j = 1, 2, ..., m is the number of objective functions; n sc is the number of soft preferences, i.e. n sc =m; Step 8.13: For the total objective function with preference Optimize and solve: After that, It is used as the objective function of the optimization problem to solve, and the optimized design variables are inserted into the original objective function to determine whether the performance indicators meet the preferences. If they meet the preferences, it exits and obtains a solution that meets the preference requirements. If not, it returns to step 8.1 to redesign the preference information.

8. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 7, It is characterized in that In step 9, the objective function containing preferences is used as the subsystem optimization objective function of the enhanced collaborative optimization strategy, and a subsystem level optimization model is established, as follows: The multi-objective optimization model of the i-th subsystem in ECO form is: Find x=[x i x Li s j ] s.t.g i (x i ,x Li )≤0 s j ≤0,j={1,…,n},j≠i The PP-ECO optimization model at the i-th subsystem level is: Find x=[x i x Li s j ] s.t.g i (x i ,x Li )≤0 s j ≤0,j={1,…,n},j≠i The IECO-PP optimization model of the i-th subsystem is: Find x=[x i x Li s j ] s.t.g i (x i ,x Li )≤0 s j ≤0,j={1,…,n},j≠i。 9. The structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization according to claim 8, It is characterized in that In step 11, the comprehensive penalty coefficient and design parameters are updated as follows: If the PP-ECO method is used, jump to step 3 to update the design parameters; if the IECO-PP method is used, jump to step 3 to update the design parameters and update the comprehensive penalty coefficient λ i , the formula is: In the formula, γ i is the incremental factor of the comprehensive penalty coefficient, γ i The value is between 0 and 100, i represents the i-th subsystem, and n is the number of iterations.

10. A structural dynamics problem optimization device based on physical planning and enhanced collaborative optimization, It is characterized in that The device is used to implement the structural dynamics problem optimization method based on physical planning and enhanced collaborative optimization as described in claim 8 or 9, and the device includes an initialization module, a CVM optimization solution module, a physical planning module, a subsystem-level optimization solution module, a system-level optimization solution module, a convergence judgment module and an output processing module; The initialization module is configured to initialize parameters for the user, and drives the following units to perform the following initialization and subsequent parameter update steps: divide the subsystem unit to divide the subsystem optimization problem; set the convergence accuracy unit to set the specified accuracy required for system-level optimization; set the optimization parameter unit to set the consistency penalty coefficient λ in the PP-ECO method Ci and the slack variable coefficient λ Fi Parameter or comprehensive penalty coefficient λ in IECO-PP i ; Set the optimization starting point unit, set the starting point of each subsequent optimization system; Penalty coefficient update unit, update the comprehensive penalty coefficient λ in the IECO-PP method i ; The CVM optimization solution module is used to configure the solver parameters initially set by the user and drive the following units to perform the optimization solution steps: a CVM objective function setting unit, which defines the objective function in the CVM optimization; a CVM constraint condition setting unit, which sets the constraint conditions in the CVM optimization solution; a CVM solver setting unit, which configures the solver parameters for solving the CVM optimization problem; a CVM communication unit, which realizes the communication between the CVM optimization solution module and other optimization modules and transmits data and information; The physical planning module drives the following units to perform the PP step: an objective function setting unit to set the objective function in the PP; a preference An interval setting unit, for setting the interval of the user's objective function preference value; An objective function preference function generating unit, generating a preference function of the objective function; A comprehensive preference function generating unit, generating a comprehensive preference function; The subsystem-level optimization solution module drives the following units to execute the subsystem optimization steps: a subsystem solver setting unit, which configures the solver parameters for subsystem optimization solution; a subsystem objective function setting unit, which sets the objective function of the subsystem; a subsystem constraint condition setting unit, which sets the constraint conditions of the subsystem; a subsystem communication unit, which realizes the communication between the subsystem and other optimization modules and transmits data and information; The system-level optimization solution module drives the following units to perform the system-level optimization steps: a system-level objective function setting unit, which sets the system-level objective function; a system-level constraint condition setting unit, which sets the system-level constraint condition; a system-level solver setting unit, which configures the solver parameters for system-level optimization solution; a system-level communication unit, which realizes the communication between the system-level optimization solution module and other modules; The convergence judgment module drives the following units to judge whether the optimization process has reached the convergence standard: the judgment unit judges the current optimization process to determine whether the preset convergence standard has been reached; The feedback unit feeds back the judgment result to the initialization module, and adjusts the parameters or terminates the optimization process as needed; The output processing module drives the following units to perform processing and output optimization results: the result processing unit processes the optimization solution results; the result output unit outputs the final optimization result; The above modules are composed of at least one processor, at least one user interface or output interface, a memory and at least one communication bus; the communication bus is used to realize the connection and communication between the modules; the user interface includes a display, a keyboard or a pointing device; the memory includes a high-speed RAM or a non-volatile memory; Operating system, which includes various system programs used to implement various basic services and handle hardware-based tasks; The application includes various application programs for realizing various application businesses, including an application program for optimizing a structural dynamics problem based on physical planning and enhanced collaborative optimization, and the application program runs under an operating system; the memory also includes a cache unit, and the cache unit is used to store cache data and optimization results generated during the optimization process.

Images