83 results about "Biobjective optimization" patented technology

The invention provides an optimization method and system for pump-storage-group double-target guide-vane closure based on double-target optimization. The optimization method includes the specific steps that the group rotating speed and volute water hammer pressure at an initial moment are collected; a pump-storage-group transient process simulation model is established, model output is the group rotating speed and the volute water hammer pressure, and model input is the guide-vane closure rule; under the constraint conditions of a control parameter and a target function, a double-target optimization function serves as a target, the pump-storage-group transient process simulation model is iteratively solved, and an optimization control parameter is obtained. According to the optimization method and system, the rotating speed index and the water hammer pressure index are simultaneously optimized, and a closure rule optimization scheme set can be obtained by solving the optimization modelone time; the opposition characteristic of the target is fully responded, and the optimization efficiency of the guide-vane closure rule is remarkably improved.

The invention discloses a motorized spindle structure optimization method based on parametric finite element model. The method is characterized by comprising the following steps of: according to a to-be-optimized motorized spindle, initially defining a design variable and an optimization goal of an optimization problem; 2) taking the design variable as a variable parameter, and establishing a motorized spindle parametric finite element model; 3) according to a motorized spindle design principle and related theories, determining value range of the design variable and determining a state variable, establishing a constraint condition, and then establishing an objective function; 4) selecting an intelligent optimization algorithm to perform optimization solving, and obtaining an optimization result. The method optimization based on the motorized spindle parametric finite element model, nonlinear bearing stiffness is considered, the established model is more close to practice, the model is flexible, the optimization effect is good, and thus, the method is particularly suitable for dual-goal optimization of high stiffness and light weight of the motorized spindle. The method can effectively improve stiffness of the motorized spindle and reduce mass of the motorized spindle, and provides effective guidance for design of the motorized spindle.

The invention relates to a robust optimization method based on an average gradient value and improved multi-objective particle swarm optimization. The robust optimization method comprises the following steps: (1) determining technological parameters to be optimized according to actual engineering situations and an optimization objective, (2) determining constraint conditions of the technological parameters to be optimized according to the actual engineering situations, (3) establishing an objective function model of the optimization objective and the technological parameters to be optimized, (4) obtaining the discrete relation between the optimization objective and welding technological parameters through a finite test, (5) calculating an objective function according to the discrete relation, (6) setting an uncertain region and calculating the robustness through the average gradient value of sample points of the uncertain region, (7) carrying out bi-objective optimization solution with the objective function and the robustness as two optimization sub-objectives. According to the robust optimization method, evaluation of the robustness and calculation of the original objective function are taken as the two optimization sub-objectives at the same time, and therefore a designer can select a reasonable compromise solution between a performance index and the amplitude of variation of the uncertain region according to actual needs.

A computer program product and a system are provided. The product and system provide associations between individuals having health care needs and available social services. The providing is efficient and cost effective.

The present application relates to a neural network optimization method. The neural network optimization method comprises the following steps: presetting modeling parameters, wherein the modeling parameters include network parameters and hardware parameters; Constructing a neural network energy consumption model based on the modeling parameters; The neural network energy consumption model and theneural network time model are subjected to double-objective optimization. The present application models the neural network in terms of time and energy consumption from the viewpoint of the hardware computing flow of the network, At the same time, the dominant modeling parameters of time and energy consumption are analyzed. The neural network model is improved by improving the modeling parameters,array segmentation method and buffer segmentation method to optimize the time and energy consumption of neural network.

The invention discloses a robot casting production line capacity optimization method. The method comprises the following steps of S1, obtaining the relevant parameters of working procedures on a robot casting production line, and setting the number of working sections; S2, setting the number of process distribution schemes, generating the variables by using a chaos sequence, mapping the variables into corresponding process numbers, then setting constraint conditions, and distributing the processes in the set working sections to obtain a process distribution scheme; S3, constructing a double-objective optimization model of the robot casting production line; and S4, respectively substituting the multiple process distribution schemes into a double-target optimization model of the robot casting production line, and calculating an optimal solution. According to the invention, the existing working procedures are reasonably distributed to each set working section, and a process distribution scheme that the balance rate is as high as possible and the smoothness index is as low as possible is calculated by utilizing the set double-target optimization model, so that the load of each section on the production line is balanced to the maximum extent, the production efficiency is improved, and the capacity optimization of the production line is realized.

The invention discloses a double-objective optimization method and system for a pumped storage unit starting rule, and is used for optimizing a decision variable when a pumped storage unit water turbine is subjected to working condition starting. The method specifically comprises the following steps that: according to a pumped storage unit regulation system, establishing a simulation model; takingrevolving speed overshoot and revolving speed raising time, establishing a double-objective optimization function; under a constraint condition of the decision variable, a revolving speed curve and atarget function, carrying out iterative solving a pumped storage unit regulation system simulation model, and obtaining an optimal guide vane starting rule set. By use of the method, the evolving speed overshoot and the revolving speed raising time are selected as to optimization objectives, one group of optimal solutions can be searched, more selection choices are provided for decision makers, and a solution selected through decision represents an optimal starting rule. Optimal starting rule can make the working condition starting time of the pumped storage unit water turbine shorter, the revolving speed overshoot of the unit is smaller, the revolving speed regulation time of the unit is shorter, and the dynamic characteristic quality of a system transition process is better.

The present invention relates to comprehensive energy system planning technology, and specifically relates to a comprehensive optimization method for both supply and demand sides of an integrated energy system that takes into account user behavior analysis, including the following steps: Step 1, analyzing user heating behavior on the demand side; Model; step 3, establish a dual-objective model and solve it. This method combines the utility function to analyze the heating behavior of users on the demand side, and establishes the supply-side economic model and the demand-side economic model according to the heating characteristics and mechanisms of different energy sources. The dual-objective optimization is carried out with the goal of the lowest electricity cost, realizing the comprehensive optimization of the comprehensive energy system considering both sides of supply and demand. Its advantages are: user behavior analysis is performed for the first time in the economic modeling of the supply side, and the two-way effect of the demand side and the supply side is considered for the first time in the optimization process, which improves the rationality and effectiveness of optimization.

A pulp-dressing technology and an optimizating method in pulp-dressing are provided in the invention. It is a multiple-stage countercurrent washing technology .Plasm becomes cleaner from the first stage to the third stage while the washer solvent becomes less concentrated .We adopt double-aim optimazing method which includes an elementary step by orthogonal and diluting factor method; found a dynamic mathematic model by NN method; produce a static model sample collection. Advantages are cleanness and optimal black liquor desity.

The invention provides a job scheduling method based on a dual target optimization genetic algorithm. The method comprises the following steps of: generating an initial population; intersecting; mutating; evaluating; selecting; adding one to the number of genetic generations; calculating a priority function h(x) for each individual in the last population; and taking a minimum individual of the h(x) as a final solution, wherein a job scheduling sequence corresponding to the individual is a final result of the algorithm.

The invention discloses a multi-target optimization resource distribution algorithm of a MU-MIMO system in a degenerate-rank of channel. The algorithm comprises the following steps: step one, establishing a system model, and analyzing user capacity upper limit and equivalent channel status when being located in rich scattering environment and non-rich scattering environment; step two, establishinga dual-target optimization problem model; and step three, solving the dual-target optimization problem, realizing the dual-target optimization of system effective throughput and energy efficiency through the power distribution and user selection; since the dual-target optimization problem is not a standard convex optimization problem and cannot be solved by directly utilizing the convex optimization technology, the optimization problem is solved through a Lagrange dual algorithm. The energy efficiency and spectrum efficiency of the system effective throughput and energy efficiency system canbe optimized when the user delay and the service rate upper limit and lower limit are simultaneously guaranteed through power distribution and user selection.

The invention discloses an average gradient value and improved multi-objective particle swarm optimization based robust optimization system. The average gradient value and improved multi-objective particle swarm optimization based robust optimization system comprises a parameter receiving module, an objective function obtaining module, a robustness calculation module, a function integration module and a function solving module; the parameter receiving module is used for receiving process parameters to be optimized according to the engineering actual situation and an optimization objective; the objective function obtaining module is used for obtaining an objective function model of the optimization objective and the process parameters to be optimized; the robustness calculation module is used for setting an uncertain domain and calculating the robustness through an average gradient value of uncertain domain samples; the function integration module is used for enabling an objective function and the robustness to form a double-objective optimization objective function; the function solving module is used for performing double-objective optimum value solution with the objective function and the robustness as the optimization objective. According to the average gradient value and improved multi-objective particle swarm optimization based robust optimization system, the applicable engineering problem range is broad and the evaluation of the robustness and the calculation of the original objective function serve as two optimization sub-objectives simultaneously to enable a designer to obtain a reasonable compromise solution between the performance index and the variation amplitude generated by the uncertain domain according to actual needs.

The invention discloses a three-dimensional-grid dual-watermark method based on a grouping strategy and neighborhood relationship positioning. In the invention, through firstly embedding a robust watermark and then embedding a fragile watermark, copyright protection and content authentication are performed on a three-dimensional model. For the robust watermark, the distribution characteristic of three-dimensional model vertexes is used to group the vertexes so as to embed watermark information. For the fragile watermark, the watermark is embedded by adjusting the geometrical relationship between model vertexes and a ring neighborhood. In the method, a dual-target optimization model is used to carry out dual-watermark coupling processing so as to solve the problem of mutual interference between the robust watermark and the fragile watermark. By using the method, the watermark is extracted without an original grid, and the robust watermark and the fragile watermark are not mutually interfered. And simultaneously, the robust watermark can resist common attacks and the attacks comprise geometric transformation, a noise, vertex reordering, simplification, and subdivision. The fragile watermark has a tamper positioning function.

The invention discloses an aviation cargo delivery method. The method comprises the following steps: 1, obtaining flight data and mail order data; 2, the flights in the same flight segment of the sameday are sorted with the sequence of the take-off time as the sequence; 3, aiming at delivery of all the mail orders in the same flight segment on the same day, establishing a secondary target planning model taking the urgency degree of delivery as a target, and the constraint conditions of the secondary target planning model comprise an adjustment parameter [beta]; 4, iteratively adjusting the value of the [beta], and solving the secondary target planning model after each iteration; and establishing a main target planning model taking the balance of the flight utilization rate as a target soas to judge an optimal scheme obtained by solving in the iteration process. According to the method, the urgency degree of the delivery of the mail order and the balance of the utilization rate of theflight in the same flight segment are comprehensively considered, and double-objective optimization in the aviation mail delivery process is realized.

The invention discloses a dynamic process monitoring method based on a bi-objective optimization algorithm, which aims to decompose the autocorrelation feature components from data while considering two manifestations of autocorrelation. The method comprises the following steps: firstly converting two representations of autocorrelation into two optimal objective functions; then, solving the optimal projection transformation matrix by repeat iteration of two optimal targets; and finally, after separating the autocorrelation from the sample data, performing online monitoring on the error. The dynamic process monitoring method based on the bi-objective optimization algorithm has advantages that: the bi-objective optimization algorithm involved in the method of the invention is a completely new modeling algorithm, and considers two manifestations of the autocorrelation at the same time, which aims at separating the feature components of temporal autocorrelation. In addition, the dynamic process monitoring method based on the bi-objective optimization algorithm can eliminate the negative influence of the temporal autocorrelation by using the technique of error real-time monitoring afterseparating the autocorrelation from the sample data. And therefore, the method of the dynamic process monitoring method based on the bi-objective optimization algorithm is more suitable for dynamic process monitoring.

The invention discloses a cloud manufacturing multi-task scheduling method based on utility perception. The method comprises the steps: building a target function through employing the maximization ofcomprehensive utility of a customer and minimization of the comprehensive utility of a manufacturer, and solving a double-target optimization problem through employing an improved quick non-dominatedsorting genetic algorithm, wherein each chromosome of the genetic algorithm includes a subtask sequence chain and a service allocation chain; and carrying out corresponding crossover mutation operation respectively, then executing a local search strategy, and finally, sorting non-dominated solutions in a solution set through a game theory, and outputting the chromosome sorted to be the first chromosome to serve as an optimal scheduling scheme. The scheduling method provided by the invention not only can meet personalized requirements of the customers, but also can improve a utilization rate of manufacturing resources of the manufacturers.

The dual-objective network planning model proposed in the dual-objective traffic network planning model optimization calculation method provided by the present invention can avoid the defects of the budget planning model and the fixed cost model, and is a more reliable model. The key of the present invention is to establish the internal connection between the optimal solution set of the scalarization problem and the non-inferior solution set of the initial dual-objective problem, and to search for the order and method of determining Pareto-optimal solutions. The invention proposes a dichotomous solution framework for the dual-objective optimization problem, and constructs a single-objective function in the process of solving the discrete optimal problem, transforming the original problem into an optimal problem of solving parameter scalarization.

The invention belongs to the technical field of aero-engine rotor stacking assembly, and provides a method for establishing a rotor stacking precision prediction model based on a geometric algebra theory. The method includes: measuring an engine rotor through precision measurement equipment, determining the position and posture of each part, and obtaining translation and rotation parameters of anupper end surface coordinate system of each part relative to a lower end surface coordinate system through calculation; utilizing a geometric algebra theory to carry out stacking modeling on each stage of parts of each rotor, and determining upper and lower end surface coordinate systems and axis vectors of each stage of rotor; calculating the concentricity and unbalance of the rotor assembly according to the stacking model; and performing dual-objective optimization on the concentricity and unbalance of the rotor assembly by using a genetic algorithm to obtain a group of optimal assembly schemes. According to the method, the calculation efficiency of aero-engine rotor stacking assembly optimization can be improved, meanwhile, it can be guaranteed that the unbalance amount and concentricity meet the requirements at the same time through double-target optimization and are in the optimal state, the assembly precision is improved, and vibration is reduced.

The invention discloses an aircraft engine rotor assembly measuring device based on three-point weighing and a double-objective optimization method. The double-objective optimization method comprisesthe following steps: determining the angle positioning of a rotary table based on a circular grating; ensuring the measurement and assembling of the concentricity, the parallelism and the center-of-mass coordinate of a measured rotor under the same reference based on an aligning and inclination adjusting device; based on a four-probe measuring device, respectively extracting concentricity errors of radial assembly surfaces and parallelism errors of axial assembly surfaces of all levels of rotors; based on a three-point weighing measuring device, respectively extracting radial coordinates of the mass centers of all levels of rotors; based on a rotor assembly pose transfer model, taking the coaxiality and center-of-mass offset of a rotor assembly as double optimization objectives, and obtaining the optimal assembly angle of each level of rotor through genetic optimization. The method can effectively solve the problem that coaxiality and an unbalance value exceed standard after the rotorof the aircraft engine is assembled, and has the characteristics of integrated measurement of the geometric and quality characteristics of the rotor, high one-time assembly qualification rate and reduction of engine vibration.

The invention discloses a method for actively power distribution network bi-layer wind-power planning based on improved Cuckoo search algorithm. The method includes a first step of establishing a bi-layer planning model, a second step of carrying out load flow calculation and linearizing the load flow calculation, a third step of establishing the Cuckoo search algorithm containing Levy flight function of sinusoidal carrier, a fourth step of converting the bi-layer planning model into a single-layer single-target planning model, a fifth step of creating a bi-target optimization problem according to the single-layer single-target planning model, and a sixth step of solving the bi-target optimization problem in the fifth step through the Cuckoo search algorithm containing Levy flight function of sinusoidal carrier in the third step. The Levy flight of the sinusoidal carrier better limits random solutions within the constraint scope of decisive variables, and partial accurate searching optimization can be supported. The bi-target optimization problem forces the infeasible solutions to move to the feasible solution area, and the feasible solutions are improved gradually. The solution optimization speed is accelerated, and the algorithmic efficiency is improved.