Methods, apparatus and equipment for determining compression spring parameters

Abstract

This application provides a method, apparatus, and device for determining compression spring parameters, relating to the field of data processing technology. This method, based on a swarm intelligence algorithm, integrates expert experience to set the gravity line for vine growth. This can reduce iteration time when iterating according to the gravity line. Furthermore, the algorithm for simulating vine growth can explore extensively in the global scope, while also conducting fine searches in local areas, helping to avoid getting trapped in local optima and increasing the probability of finding the global optimum.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, apparatus, and equipment for determining the parameters of a compression spring. Background Technology

[0002] Before large and complex systems are put into use, it is necessary to set their important parameters. Currently, the traditional method mainly relies on experienced engineers for manual optimization. Engineers first obtain possible initial values ​​for the parameters based on their expert experience, then perform long-term manual operations to continuously optimize the parameters in the simulation system, and finally embed the manually optimized parameters into the system for use. However, this method has problems such as high cost of human training, time-consuming and labor-intensive optimization process, and poor consistency of optimization results.

[0003] To address these issues, researchers have introduced swarm intelligence algorithms. However, in these complex systems, traditional swarm intelligence algorithms such as Genetic Algorithm (GA), Whale Algorithm (WOA), and Grey Wolf Algorithm (GWO) suffer from slow convergence speed, susceptibility to local optima, and inability to integrate expert experience. Summary of the Invention

[0004] This application provides a method, apparatus, and device for determining compression spring parameters, which can improve the speed of parameter optimization by integrating expert experience.

[0005] This application provides a method for determining the parameters of a compression spring, including:

[0006] Obtain the parameters to be optimized and the fitness function corresponding to the parameters to be optimized, wherein the parameters to be optimized are parameters of a complex system;

[0007] The vines in the vine population are initialized to determine the initial values ​​of the parameters to be optimized, and the initial values ​​of the parameters to be optimized are determined as the initial iteration values ​​of the optimal position.

[0008] Gravity lines are determined in the solution space of the parameter to be optimized based on expert experience. The gravity lines are lines connecting the position of the parameter to be optimized in the solution space, determined based on expert experience, to the midpoint of the solution space.

[0009] The iteration is performed based on the gravity line, the initial iteration value of the optimal position, and the fitness function. The iteration is completed when the iteration stopping condition is met, and the target optimal position is obtained. The target optimal position is then determined as the target value of the parameter to be optimized.

[0010] In one embodiment of this application, initializing the vines in the vine population includes:

[0011] Perform chaotic mapping initialization on each vine in the vine population.

[0012] In one embodiment of this application, iteration is performed based on the gravity line, the initial iteration value of the optimal position, and the fitness function, including:

[0013] Based on the current iteration number, the total number of iterations, and the number of vines, determine the first vine number and the second vine number in the current iteration process; the first vine number is the number of vines explored in the vine population, and the second vine number is the number of vines developed in the vine population;

[0014] Based on the fitness function and the optimal position, the update position of the exploration vine is determined; the exploration vine is the number of vines that are the first vines in the vine population.

[0015] Determine the update location of the developed vines, wherein the developed vines are the second-to-last vines in the vine population;

[0016] Based on exploring the regeneration location of vines, developing the regeneration location of vines, and gravity lines, the regeneration location of each vine after it droops in the vine population is determined.

[0017] Based on the vine's original position, explore the vine's update position, develop the vine's update position, and the update position after the vine droops, update the optimal position.

[0018] In one embodiment of this application, determining the number of first vines and second vines in the current iteration process based on the current iteration number, the total number of iterations, and the number of vines includes:

[0019] The first iteration factor is determined based on the total number of iterations and the current number of iterations;

[0020] The number of the first vines is determined based on the first iteration factor;

[0021] The number of second vines is determined based on the number of vines and the number of first vines.

[0022] In one embodiment of this application, determining the update position of the exploration vine based on the fitness function and the optimal position includes:

[0023] The second iteration factor is determined based on the total number of iterations and the current number of iterations;

[0024] Based on the second iteration factor, the current iteration position, and the previous iteration position, determine the first position of the explored vine;

[0025] A first value is determined based on the fitness function and the current iteration position, and a second value is determined based on the fitness function and the first position.

[0026] When the first value is greater than or equal to the second value, the update position of the exploration vine is determined based on the optimal position, the random matrix, and the current iteration position;

[0027] When the first value is less than the second value, the first position is determined as the updated position of the exploration vine.

[0028] In one embodiment of this application, determining the update location of the development vine includes:

[0029] Based on the first iteration factor, determine the control parameters;

[0030] When the control parameter is greater than the first preset value, leaf-shaped line development is performed based on the optimal position and the current iteration position to obtain the updated position of the developed vine;

[0031] When the control parameter is less than or equal to the first preset value, a cardioid is developed based on the optimal position and the current iteration position to obtain the updated position of the development vine.

[0032] In one embodiment of this application, updating the optimal position based on the original position of the vine, the updated position of the explored vine, the updated position of the developed vine, and the updated position after the vine droops includes:

[0033] The iterative update position is determined based on the original position of the vine, the exploration of the vine's update position, the development of the vine's update position, and the update position after the vine droops.

[0034] The location of the competition center is determined based on the iterative update position of each vine;

[0035] Update the optimal position based on the location of the competition center and the optimal position in the current iteration.

[0036] In one embodiment of this application, the iteration stopping condition includes the current iteration count reaching the total number of iterations.

[0037] To achieve the above and other related objectives, this application provides a compression spring parameter determination device, comprising:

[0038] The data acquisition module is used to acquire the parameters to be optimized and the fitness function corresponding to the parameters to be optimized, wherein the parameters to be optimized are parameters of a complex system;

[0039] An initialization module is used to initialize the vines in the vine population, determine the initial values ​​of the parameters to be optimized, and determine the initial values ​​of the parameters to be optimized as the initial iteration values ​​of the optimal position.

[0040] The gravity line determination module is used to determine the gravity line in the solution space of the parameter to be optimized based on expert experience. The gravity line is the line connecting the position of the parameter to be optimized in the solution space determined based on expert experience and the midpoint of the solution space.

[0041] The iteration module is used to iterate based on the gravity line, the initial iteration value of the optimal position, and the fitness function. The iteration is completed when the iteration stopping condition is met, the target optimal position is obtained, and the target optimal position is determined as the target value of the parameter to be optimized.

[0042] To achieve the above and other related objectives, this application also provides an electronic device, the electronic device comprising:

[0043] One or more processors;

[0044] Memory used to store the executable program code of the processor;

[0045] The processor is configured to execute the program code to implement the above-described method for determining the parameters of the compression spring.

[0046] As described above, the method, apparatus, and device for determining compression spring parameters provided in this application have the following beneficial effects:

[0047] This application discloses a method for determining the parameters of a compression spring. This method, based on a swarm intelligence algorithm, integrates expert experience to set the gravity line for vine growth. This reduces the iteration time when iterating according to the gravity line. Furthermore, the algorithm for simulating vine growth can explore extensively in the global scope and also perform fine searches in local areas, helping to avoid getting trapped in local optima and increasing the probability of finding the global optimum.

[0048] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. Attached Figure Description

[0049] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0050] Figure 1 This is a flowchart illustrating a method for determining compression spring parameters, as shown in an exemplary embodiment of this application;

[0051] Figure 2This is a flowchart illustrating a method for determining compression spring parameters, as shown in another exemplary embodiment of this application;

[0052] Figure 3 This is a structural block diagram of a compression spring parameter determination device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0053] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0054] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0055] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0056] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for determining compression spring parameters, as shown in an exemplary embodiment of this application. (Reference) Figure 1 It can be seen that the method for determining the parameters of this compression spring may include:

[0057] Step S110: Obtain the parameters to be optimized and the fitness function corresponding to the parameters to be optimized.

[0058] Among them, the parameters to be optimized are those of a complex system.

[0059] In one embodiment of this application, before a large, complex system is put into use, it is necessary to set important parameters. The parameter to be optimized can be an important parameter in the complex system. The fitness function corresponding to the parameter to be optimized can be designed by the designer based on the complex system.

[0060] For example, when the complex system is a compression spring, the goal of compression spring design is to minimize its mass under certain constraints. The mass of the compression spring is the parameter to be optimized, which includes four inequality constraints: minimum deflection, shear stress, oscillation frequency, and outer diameter limit. The diameter of the spring wire is also a factor. Average diameter of spring coil and the effective number of spring coils With 3 decision variables, the fitness function includes: ;

[0061] The constraints that need to be satisfied include:

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] The boundary conditions for each decision variable are as follows: .

[0067] For example, when the complex system is a gear system, the goal is to minimize the gear ratio, which is the parameter to be optimized. The gear ratio is defined as the ratio of the output shaft angular velocity to the input shaft angular velocity. The number of teeth on the gear... , , , If the decision variable is determined, then the fitness function includes: ;

[0068] in, .

[0069] For example, when the complex system is a reinforced concrete beam, assume the beam has a span of 30 feet and bears a live load of 2000 lbf and a dead load of 1000 lbf, including the beam's weight. The concrete compressive strength is 5 ksi, and the steel yield stress is 50 ksi. The concrete cost is $0.02 / in² / linear ft, and the steel cost is $1.0 / in² / linear ft, where $0.02 / in² / linear ft means the unit cost per square inch per linear foot is $0.02. To minimize the total cost of the structure, the area of ​​the steel reinforcement is determined. , beam width and the depth of the beam Given three decision variables, with the beam's depth-to-width ratio restricted to less than or equal to 4, and the total cost of the reinforced concrete beam as the parameter to be optimized, the fitness function includes: ;

[0070] The constraints that need to be satisfied include:

[0071] ;

[0072] ;

[0073] in, .

[0074] It should be noted that, in the above embodiments, Represents the fitness function. This represents any value of the parameter to be optimized. , , , These represent the decision variables for the parameters to be optimized. , , , Let each represent a constraint condition. When any constraint condition is not met, the fitness function... It will be set to an extremely large value.

[0075] It should be noted that the method for determining the compression spring parameters provided in this application embodiment can be implemented by a server or server cluster or other devices.

[0076] Step S120: Initialize the vines in the vine population, determine the initial values ​​of the parameters to be optimized, and set the initial values ​​of the parameters to be optimized as the initial iteration values ​​of the optimal position.

[0077] In one embodiment of this application, chaotic mapping initialization can be performed on each vine in the vine population to determine the initial values ​​of the parameters to be optimized. Chaotic mapping initialization on each vine in the vine population ensures that the initial position values ​​of each vine are distributed around the solution space. The number of vines and the total number of iterations in the vine population, pre-set by the designer, can be obtained; these two metrics can be used to balance optimization time and optimization accuracy. The position of each vine in the vine population represents the possible values ​​of the parameters to be optimized.

[0078] For example, each individual vine in the vine population can be initialized with a chaotic map three times.

[0079] (1)

[0080] (2)

[0081] in, It is the position matrix of the vine population. It refers to the number of vines. It is The location of the vine It is The first vine One decision variable, It is the number of decision variables. It is The lower bound of each decision variable. It is The upper bound of each decision variable. and Used to set the solution space. This indicates the initialization of the chaotic mapping.

[0082] Step S130: Determine the gravity line in the solution space of the parameter to be optimized based on expert experience. The gravity line is the line connecting the position of the parameter to be optimized in the solution space determined based on expert experience with the midpoint of the solution space.

[0083] In one embodiment of this application, a gravity line can be determined in the solution space of the parameter to be optimized based on expert experience. The gravity line is a line connecting the position of the parameter to be optimized in the solution space determined based on expert experience with the midpoint of the solution space.

[0084] In one possible implementation, the center point of the solution space can be obtained, expert experience points can be determined based on expert experience, and the line connecting the center point and the expert experience points can be determined as the gravity line.

[0085] For example, the position of the vine can include three decision variables: x, y, and z. The solution space is [0, 1]. The expert experience point determined by expert experience can be (0.5, 0.5, 1), and the center point of the solution space can be (0.5, 0.5, 0.5). Then, the line connecting the center point of the solution space and the expert experience point can be determined as the gravity line. The solution space can be determined by the value range of each decision variable of the parameter to be optimized.

[0086] Step S140: Iterate according to the gravity line, the initial iteration value of the optimal position, and the fitness function. The iteration is completed when the iteration stopping condition is met, the target optimal position is obtained, and the target optimal position is determined as the target value of the parameter to be optimized.

[0087] In one embodiment, the iterative process in step S140 based on the gravity line, the initial iterative value of the optimal position, and the fitness function may include steps S141 to S145.

[0088] Step S141: Based on the current iteration number, the total number of iterations, and the number of vines, determine the number of the first vine and the number of the second vine in the current iteration process.

[0089] The first number of vines refers to the number of vines explored within the vine population, while the second number of vines refers to the number of vines developed within the vine population.

[0090] In one embodiment of this application, determining the number of first vines and the number of second vines in the current iteration process based on the current iteration number, the total number of iterations, and the number of vines may include: determining a first iteration factor based on the total number of iterations and the current iteration number; determining the number of first vines based on the first iteration factor; and determining the number of second vines based on the number of vines and the number of first vines.

[0091] For example, the process of determining the number of first vines and the number of second vines in the current iteration may include:

[0092] (3)

[0093] (4)

[0094] in, It is the number of the first vine. It is the second number of vines. It is a rounding function. It is the first iteration factor. It is the total number of iterations. It is the current iteration number.

[0095] Step S142: Based on the fitness function and the optimal position, determine the update position of the exploration vine; the exploration vine is the number of vines that are the first vines in the vine population.

[0096] In one embodiment of this application, determining the update position of the exploration vine based on the fitness function and the optimal position may include: determining a second iteration factor based on the total number of iterations and the current iteration number; determining a first position of the exploration vine based on the second iteration factor, the current iteration position, and the previous iteration position; determining a first value based on the fitness function and the current iteration position, and determining a second value based on the fitness function and the first position; when the first value is greater than or equal to the second value, determining the update position of the exploration vine according to the optimal position, the random matrix, and the current iteration position; when the first value is less than the second value, determining the first position as the update position of the exploration vine.

[0097] For example, the process of determining the update location of the vine can characterize the behavior of climbing the host tree, i.e., the algorithm exploration phase, in which the vine population is at the forefront. A single vine will perform this behavior, which may include:

[0098] The vines continue to grow along their direction of growth:

[0099] (5)

[0100] (6)

[0101] Indicates the first The vines are iterating The position at that time is also the current iteration position; For the first The vines are iterating The position at that time is also the position of the previous iteration; Indicates the first The vines are iterating The new position after continuous growth along the growth direction is the first position; It is the second iteration factor. It is a matrix of random numbers between 0 and 1.

[0102] It should be noted that during the first iteration, the "current iteration position" is the position of each individual vine after the initialization of the vine population. After one iteration, this position will change to the updated iteration position. During the second iteration, the "current iteration position" is the updated iteration position of the vine individual after the completion of the first iteration.

[0103] The vines search for new host trees, when If it is not a better option, the vine will perform a random search using Levi's Flight to find a new host tree:

[0104] (7)

[0105] in, Indicates the optimal position; Indicates iteration After finding a new host tree in the middle The new location of the vine, that is, the updated location of the vine; Represents the fitness function; It is a random number matrix generated by Levi's flight.

[0106] Step S143: Determine the update location for the development vine.

[0107] Among them, the number of vines developed is the second-to-last vine in the vine population.

[0108] In one embodiment of this application, determining the update position of the development vine may include: determining control parameters based on a first iteration factor; when the control parameters are greater than a first preset value, developing leaf-shaped lines based on the optimal position and the current iteration position to obtain the update position of the development vine; when the control parameters are less than or equal to the first preset value, developing cardioid lines based on the optimal position and the current iteration position to obtain the update position of the development vine.

[0109] For example, determining the update position of the vines can characterize leaf growth behavior, i.e., in the algorithm development phase, in the vine population... Each vine will perform this behavior:

[0110] Set control parameters to control the growth of two leaf shapes: leaf shape and heart shape.

[0111] (8)

[0112] in, It is a random number between 0 and 1. These are control parameters.

[0113] when At that time, leaf-shaped line development and optimization are carried out at a position that is a certain distance from the optimal position to prevent the algorithm from quickly getting trapped in local optima.

[0114] when At that time, we will develop and optimize the cardioid curve, and conduct in-depth development near the optimal position:

[0115] (9)

[0116] (10)

[0117] (11)

[0118] (12)

[0119] (13)

[0120] (14)

[0121] (15)

[0122] in, In iteration The first leaf after the middle leaves grow The new location of the vine, that is, the updated location for developing the vine; It is a matrix of random numbers generated by a normal distribution; It is a polar coordinate function of the leaf shape; It is a cardioid polar coordinate function; It is the first random angle; It is the second random angle; It is the third random angle; It is the fourth random angle.

[0123] Step S144: Based on the exploration of vine renewal locations, development of vine renewal locations, and gravity lines, determine the renewal locations of each vine in the vine population after it droops.

[0124] For example, the process of determining the renewal position of each vine after it has drooped in a vine population may include:

[0125] The gravity-induced drooping behavior represents the stage of integrating expert experience. In S2.3, a gravity line is established based on expert experience, and the vines will droop downwards towards the gravity line during their growth.

[0126] (16)

[0127] (17)

[0128] in, Iteration The Middle The new position after the vine droops down, that is, the renewed position after the vine droops down; Indicates the location following the behavior of clinging to the host tree or leaf growth; For iteration Mid-gravity line and The random decision changes the same position.

[0129] Step S145: Based on the original position of the vine, the explored update position of the vine, the developed update position of the vine, and the update position after the vine droops, update the optimal position.

[0130] In one embodiment of this application, updating the optimal position based on the original position of the vine, the updated position of the exploring vine, the updated position of the developing vine, and the updated position after the vine droops may include: determining the iterative update position based on the original position of the vine, the updated position of the exploring vine, the updated position of the developing vine, and the updated position after the vine droops; determining the competition center position based on the iterative update positions of each vine; and updating the optimal position according to the competition center position and the current iterative optimal position.

[0131] For example, the process of updating the optimal position may include:

[0132] Group behavior, namely the stages of position update, group competition, and optimal value update.

[0133] 1) Position Update Behavior. The original vine position, the position after clinging to the host tree or leaf growth behavior, and the position after gravity-induced drooping behavior are compared using a fitness function to update the vine's position:

[0134] (18)

[0135] in, This is for the iterative update position. After determining the iterative update position, it can be... Updated to the number The vines are iterating The current iteration position. In other words, the iteration update position determined by the previous iteration update can be used as the current iteration position. .

[0136] 2) Group competition behavior. The vine population will continuously converge towards the center to compete for the optimal position. This behavior is simulated by randomly selecting three vine individuals from the population:

[0137] (19)

[0138] in, For random number The iterative update position of the vine. It is the central position in the competition.

[0139] 3) Compare the optimal value with the position of the competition center, and update the optimal position:

[0140] (20)

[0141] For example, please refer to Figure 2 The flowchart illustrates a method for determining compression spring parameters, which is another exemplary embodiment of this application.

[0142] Figure 3 This is a block diagram illustrating a compression spring parameter determining device according to an exemplary embodiment of this application. Figure 3 As shown, the exemplary compression spring parameter determining device 300 includes:

[0143] Data acquisition module 310 is used to acquire the parameters to be optimized and the fitness function corresponding to the parameters to be optimized. The parameters to be optimized are parameters of a complex system.

[0144] The initialization module 320 is used to initialize the vines in the vine population, determine the initial values ​​of the parameters to be optimized, and set the initial values ​​of the parameters to be optimized as the initial iteration values ​​of the optimal position.

[0145] The gravity line determination module 330 is used to determine the gravity line in the solution space of the parameter to be optimized based on expert experience. The gravity line is the line connecting the position of the parameter to be optimized in the solution space determined based on expert experience with the midpoint of the solution space.

[0146] The iteration module 340 is used to iterate based on the gravity line, the initial iteration value of the optimal position, and the fitness function. The iteration is completed when the iteration stopping condition is met, the target optimal position is obtained, and the target optimal position is determined as the target value of the parameter to be optimized.

[0147] It should be noted that the compression spring parameter determination device and the compression spring parameter determination method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the compression spring parameter determination device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0148] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the compression spring parameter determination method provided in the above embodiments.

[0149] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the compression spring parameter determination method provided in the above embodiments. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0150] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the compression spring parameter determination method provided in the various embodiments described above.

[0151] In the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "comprising" and "including" as used throughout the specification and claims are open-ended terms and should therefore be interpreted as "comprising but not limited to".

[0152] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for determining the parameters of a compression spring, characterized in that, include: Obtain the parameters to be optimized and the fitness function corresponding to the parameters to be optimized, wherein the parameters to be optimized are parameters of a complex system; The vines in the vine population are initialized to determine the initial values ​​of the parameters to be optimized, and the initial values ​​of the parameters to be optimized are determined as the initial iteration values ​​of the optimal position. Gravity lines are determined in the solution space of the parameter to be optimized based on expert experience. The gravity lines are lines connecting the position of the parameter to be optimized in the solution space, determined based on expert experience, to the midpoint of the solution space. The iteration is performed based on the gravity line, the initial iteration value of the optimal position, and the fitness function. The iteration is completed when the iteration stopping condition is met, and the target optimal position is obtained. The target optimal position is then determined as the target value of the parameter to be optimized. Iteration is performed based on the gravity line, the initial iteration value of the optimal position, and the fitness function, including: Based on the current iteration number, the total number of iterations, and the number of vines, determine the first vine number and the second vine number in the current iteration process; the first vine number is the number of vines explored in the vine population, and the second vine number is the number of vines developed in the vine population; Based on the fitness function and the optimal position, the update position of the exploration vine is determined; the exploration vine is the number of vines that are the first vines in the vine population. Determine the update location of the developed vines, wherein the developed vines are the second-to-last vines in the vine population; Based on exploring the regeneration location of vines, developing the regeneration location of vines, and gravity lines, the regeneration location of each vine after it droops in the vine population is determined. Based on the vine's original position, explore the vine's update position, develop the vine's update position, and the update position after the vine droops, update the optimal position; The complex system is a compression spring, and the parameter to be optimized is the mass of the compression spring. The fitness function corresponding to the mass of the compression spring includes: ; The constraints that need to be satisfied include: ； ； ； ； in, Represents the fitness function. This indicates the mass of the compression spring. For deflection, For shear stress, The oscillation frequency is... Due to outer diameter limitations, The diameter of the spring wire. The average diameter of the spring coil. Let be the effective number of coils of the spring, and .

2. The method for determining compression spring parameters according to claim 1, characterized in that, Initialize the vines in the vine population, including: Perform chaotic mapping initialization on each vine in the vine population.

3. The method for determining compression spring parameters according to claim 1, characterized in that, Based on the current iteration number, the total number of iterations, and the number of vines, determine the number of the first vine and the number of the second vine in the current iteration process, including: The first iteration factor is determined based on the total number of iterations and the current number of iterations; The number of the first vines is determined based on the first iteration factor; The number of second vines is determined based on the number of vines and the number of first vines.

4. The method for determining compression spring parameters according to claim 1, characterized in that, Based on the fitness function and the optimal position, the update position of the exploration vine is determined, including: The second iteration factor is determined based on the total number of iterations and the current number of iterations; Based on the second iteration factor, the current iteration position, and the previous iteration position, determine the first position of the explored vine; A first value is determined based on the fitness function and the current iteration position, and a second value is determined based on the fitness function and the first position. When the first value is greater than or equal to the second value, the update position of the exploration vine is determined based on the optimal position, the random matrix, and the current iteration position; When the first value is less than the second value, the first position is determined as the updated position of the exploration vine.

5. The method for determining compression spring parameters according to claim 3, characterized in that, Determine the update locations for developing vines, including: Based on the first iteration factor, determine the control parameters; When the control parameter is greater than the first preset value, leaf-shaped line development is performed based on the optimal position and the current iteration position to obtain the updated position of the developed vine; When the control parameter is less than or equal to the first preset value, a cardioid is developed based on the optimal position and the current iteration position to obtain the updated position of the development vine.

6. The method for determining compression spring parameters according to claim 1, characterized in that, Based on the vine's original position, the vine's updated position after exploration, the vine's updated position after development, and the vine's updated position after drooping, the optimal updated position is determined, including: The iterative update position is determined based on the original position of the vine, the exploration of the vine's update position, the development of the vine's update position, and the update position after the vine droops. The location of the competition center is determined based on the iterative update position of each vine; Update the optimal position based on the location of the competition center and the optimal position in the current iteration.

7. The method for determining compression spring parameters according to claim 1, characterized in that, The iteration stopping condition includes the current iteration count reaching the total number of iterations.

8. A device for determining the parameters of a compression spring, characterized in that, include: The data acquisition module is used to acquire the parameters to be optimized and the fitness function corresponding to the parameters to be optimized, wherein the parameters to be optimized are parameters of a complex system; An initialization module is used to initialize the vines in the vine population, determine the initial values ​​of the parameters to be optimized, and determine the initial values ​​of the parameters to be optimized as the initial iteration values ​​of the optimal position. The gravity line determination module is used to determine the gravity line in the solution space of the parameter to be optimized based on expert experience. The gravity line is the line connecting the position of the parameter to be optimized in the solution space determined based on expert experience and the midpoint of the solution space. The iteration module is used to iterate based on the gravity line, the initial iteration value of the optimal position, and the fitness function. The iteration is completed when the iteration stopping condition is met, the target optimal position is obtained, and the target optimal position is determined as the target value of the parameter to be optimized. Iteration is performed based on the gravity line, the initial iteration value of the optimal position, and the fitness function, including: Based on the current iteration number, the total number of iterations, and the number of vines, determine the first vine number and the second vine number in the current iteration process; the first vine number is the number of vines explored in the vine population, and the second vine number is the number of vines developed in the vine population; Based on the fitness function and the optimal position, the update position of the exploration vine is determined; the exploration vine is the number of vines that are the first vines in the vine population. Determine the update location of the developed vines, wherein the developed vines are the second-to-last vines in the vine population; Based on exploring the regeneration location of vines, developing the regeneration location of vines, and gravity lines, the regeneration location of each vine after it droops in the vine population is determined. Based on the vine's original position, explore the vine's update position, develop the vine's update position, and the update position after the vine droops, update the optimal position; The complex system is a compression spring, and the parameter to be optimized is the mass of the compression spring. The fitness function corresponding to the mass of the compression spring includes: ; The constraints that need to be satisfied include: ； ； ； ； in, Represents the fitness function. This indicates the mass of the compression spring. For deflection, For shear stress, The oscillation frequency is... Due to outer diameter limitations, The diameter of the spring wire. The average diameter of the spring coil. Let be the effective number of coils of the spring, and .

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory used to store the executable program code of the processor; The processor is configured to execute the program code to implement the method for determining the parameters of a compression spring as described in any one of claims 1 to 7.

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