A design method, device, electronic device and medium for S-shaped front longitudinal beam parameters

Abstract

The present invention relates to a method, device, electronic device, and medium for designing parameters of an S-shaped front longitudinal beam, belonging to the technical field of intelligent optimization algorithms. The method comprises: obtaining a first population and a temporary population based on an initial population and a direction vector of a target model of the S-shaped front longitudinal beam; meshing the first population to obtain first mesh data; performing a selection operation on the first population based on the temporary population according to a probability table to generate a second population; meshing the second population to obtain second mesh data of the second population; updating the probability table based on the first mesh data and the second mesh data, the updated probability table being used for selection in the next iteration; merging the first and second populations to obtain a third population, and performing environmental selection on the third population to obtain a fourth population; iterating using the fourth population as the initial population, and determining optimal parameters of the S-shaped front longitudinal beam based on the iterated populations. The present invention improves the design accuracy of the parameters of the S-shaped front longitudinal beam.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent optimization algorithms, and in particular to a method, device and electronic equipment for designing parameters of an S-shaped front longitudinal beam. Background Art

[0002] With the vigorous development of the automobile industry, automobile sales in my country have also been rising year by year, and the passive safety of vehicles has received more and more attention from consumers and regulatory authorities.

[0003] Research shows that the vast majority of automobile accidents are caused by head-on collisions. The S-shaped front longitudinal beam connects the front and rear ends of the vehicle and is the main energy-absorbing structure during a head-on collision. It absorbs collision energy and reduces impact force. Therefore, how to enhance the vehicle's anti-collision performance during a head-on collision has become a design focus.

[0004] The problem with the existing technology for designing the parameters of the S-shaped front longitudinal beam is that the existing intelligent algorithms for solving such problems have low convergence accuracy, and the optimized results often fail to meet the accuracy requirements of the designer.

[0005] In summary, the existing technology lacks an improved multi-objective evolutionary algorithm to improve the design accuracy of S-shaped front longitudinal beam parameters. Summary of the Invention

[0006] In view of this, it is necessary to provide a method, device, electronic equipment and medium for designing parameters of an S-shaped front longitudinal beam to solve the problem of poor design accuracy of the parameters of the S-shaped front longitudinal beam.

[0007] In order to solve the above problems, the present invention provides a method for designing parameters of an S-shaped front longitudinal beam, comprising:

[0008] S1: Construct a target model of the S-shaped front longitudinal beam, generate a direction vector in the target space, and generate a random initial population in the decision space;

[0009] S2: Get the first population and temporary population based on the initial population and direction vector;

[0010] S3: gridding the first population to obtain first grid data of the first population; S4: performing a selection operation on the first population based on the temporary population according to the probability table to generate a second population;

[0011] S5: performing grid division on the second population to obtain second grid data of the second population;

[0012] S6: updating the probability table based on the first grid data and the second grid data, and the updated probability table is used for the selection operation in S4 in the next iteration step;

[0013] S7: The first and second populations are combined to obtain a third population, and the third population is subjected to environmental selection to obtain a fourth population;

[0014] S8: Repeat steps S2-S7 with the fourth population as the initial population, and determine the optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

[0015] In a possible implementation, constructing a target model of the S-shaped front longitudinal beam, generating a direction vector in a target space, and generating a random initial population in a decision space include:

[0016] Construct a target model of the S-shaped front longitudinal beam to maximize energy absorption, minimize impact force, and define the width, height, and thickness of the S-shaped front longitudinal beam;

[0017] According to the range of decision variables of the target model, random initialization is performed in the decision space to obtain the initial population;

[0018] Generates uniformly distributed direction vectors in target space.

[0019] In a possible implementation, obtaining the first population and the temporary population based on the initial population and the direction vector includes:

[0020] Determine the angle between individuals in the initial population and the direction vector;

[0021] Associating individuals in the initial population with the direction vector with the smallest angle to obtain a plurality of first associated subpopulations of the initial population;

[0022] The first population and the temporary population are determined based on the number of the first associated subpopulations and the preset number of subpopulations of the initial population.

[0023] In a possible implementation, determining the first population and the temporary population based on the number of the first associated subpopulations and the preset number of subpopulations of the initial population includes:

[0024] When it is determined that the number of the first associated subpopulations is less than a preset number of subpopulations of the initial population, a preset number of subpopulations is selected from the initial population and added to the first associated subpopulation to obtain a first population and a temporary population, where the preset number of subpopulations is the preset number of subpopulations of the initial population minus the number of the first associated subpopulations;

[0025] After determining that the number of the associated subpopulations is not less than the preset number of subpopulations of the initial population, a first population and a temporary population are obtained based on a non-dominated sorting algorithm.

[0026] In a possible implementation, the first grid data includes a grid state and a grid dominance ranking, and the grid division of the first population to obtain the first grid data of the first population includes:

[0027] Determining an upper boundary and a lower boundary of a first target space based on the preset dimensions and the target model;

[0028] Divide the target model into a preset number of parts to obtain a number of equally divided first target models;

[0029] determining the coordinates of each individual in the first population based on the upper boundary and the lower boundary;

[0030] determining a grid state of the first population based on the coordinates of each individual in the first population;

[0031] A grid dominance ranking of the first population is determined based on the grid status of the first population and a preset number of shares.

[0032] In a possible implementation, updating the probability table based on the first grid data and the second grid data includes:

[0033] The first individual in the first population produces a second individual as an offspring based on the strategy in the probability table, and the second individual is an individual in the second population;

[0034] Calculate the mesh states of the first body and the second body;

[0035] determining a grid dominance ranking of the first individual based on the grid state of the first individual;

[0036] determining a grid dominance ranking of the second individual based on the grid state of the second individual;

[0037] When it is determined that the grid dominance ranking of the second individual is lower than the grid dominance ranking of the first individual, determining the reward value to be a first preset value;

[0038] When it is determined that the grid dominance ranking of the second individual is not less than the grid dominance ranking of the first individual, determining the reward value to be a second preset value;

[0039] The probability value of the strategy in the probability table is determined based on the reward value and the current probability value of the strategy.

[0040] In a possible implementation, performing environmental selection on the third population to obtain the fourth population includes:

[0041] Calculating the angle between each individual in the third population and the direction vector;

[0042] Associating individuals in the third population with the direction vector with the smallest angle to obtain a plurality of second associated subpopulations of the third population;

[0043] When it is determined that the number of the second associated subpopulations is less than the preset number of subpopulations of the initial population, a preset number of subpopulations is selected from the three populations and added to the second associated subpopulation, where the preset number of subpopulations is the preset number of subpopulations of the initial population minus the number of the second associated subpopulations;

[0044] After determining that the number of the second associated subpopulations is not less than the preset number of subpopulations of the initial population, a fourth population is obtained based on the angle and grid dominance ranking of each individual of the third population.

[0045] On the other hand, the present invention also provides a device for designing parameters of an S-shaped front longitudinal beam, comprising:

[0046] Initialization parameter module, used to build the target model of the S-shaped front longitudinal beam, generate direction vectors in the target space, and generate random initial populations in the decision space;

[0047] A first population acquisition module, used to obtain a first population and a temporary population based on an initial population and a direction vector;

[0048] A first grid data acquisition module is used to perform grid division on the first population to obtain first grid data of the first population; a second population acquisition module is used to perform a selection operation on the first population based on the temporary population according to the probability table to generate a second population;

[0049] A second grid data acquisition module is used to perform grid division on the second population to obtain second grid data of the second population;

[0050] A probability table updating module, configured to update the probability table based on the first grid data and the second grid data, wherein the updated probability table is used for the selection operation in the next iteration step;

[0051] A population merging module is used to merge the first population and the second population to obtain a third population, and perform environmental selection on the third population to obtain a fourth population;

[0052] The loop iteration module is used to repeat steps S2-S7 using the fourth population as the initial population, and determine the optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

[0053] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein:

[0054] The memory is used to store programs;

[0055] The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for designing parameters of an S-shaped front longitudinal beam described in any one of the above implementations.

[0056] On the other hand, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the method for designing S-shaped front longitudinal beam parameters described in any of the above-mentioned implementation methods.

[0057] The beneficial effects of the present invention are as follows: the present invention provides a method for designing parameters of an S-shaped front longitudinal beam, the method comprising: constructing and generating an initial population in a decision space and a direction vector in a target space based on a target model of the S-shaped front longitudinal beam, obtaining a first population and a temporary population based on the initial population and the direction vector, performing grid division on the first population to obtain first grid data of the first population, thereby better evaluating the convergence and distribution of the solution set, performing a selection operation on the first population based on the temporary population according to a probability table to generate a second population, thereby selecting the most appropriate offspring generation strategy for individuals according to different evolutionary states, and dynamically inverting the grid state control. A feedback reward strategy is provided, wherein a dynamic feedback reward is given to the strategy based on the changes in the grid state transition dominance relationship between the parent generation, i.e., individuals of the first population, and the offspring generation, i.e., individuals of the second population, thereby influencing the preference for the generation strategy. The second population is gridded to obtain second grid data of the second population; a probability table is updated based on the first grid data and the second grid data, and the updated probability table is used for the selection operation in the next iterative step. The first population and the second population are merged to obtain a third population, and the third population is subjected to environmental selection to obtain a fourth population. The fourth population is used as the initial population for iteration, and the optimal parameters of the S-shaped front longitudinal beam are determined based on the iterated population. The present invention provides a dynamic feedback reward strategy based on the grid state dominance in generating offspring subpopulations, and provides a dynamic feedback reward strategy based on the changes in the grid state transition dominance relationship between the parent generation, i.e., individuals of the first population, and the offspring generation, i.e., individuals of the second population, thereby influencing the preference for the generation strategy. This improves the convergence accuracy of the algorithm and further improves the accuracy of the parameters of the S-shaped front longitudinal beam. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of an embodiment of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0059] Figure 2 A schematic structural diagram of a crash box provided by the present invention;

[0060] Figure 3 A schematic structural diagram of an S-shaped front longitudinal beam provided by the present invention;

[0061] Figure 4 A schematic diagram of dividing the initial population in the decision space of a design method for parameters of an S-shaped front longitudinal beam provided by the present invention;

[0062] Figure 5For the present invention Figure 1 A schematic flow chart of an embodiment of S102;

[0063] Figure 6 For the present invention Figure 1 A schematic flow chart of an embodiment of S103;

[0064] Figure 7 A schematic diagram of each target grid division in a decision space in one embodiment of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0065] Figure 8 A schematic diagram of a decision space population grid division in an embodiment of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0066] Figure 9 Two flow charts of embodiments of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0067] Figure 10 Schematic diagram of method population division for two embodiments of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0068] Figure 11 A flowchart of a method for generating offspring according to two embodiments of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0069] Figure 12 A schematic flow chart of a method for selecting an environment for a merged population in two embodiments of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0070] Figure 13 This is a diagram showing experimental comparison results in two embodiments of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0071] Figure 14 A schematic flow chart of an embodiment of a device for designing parameters of an S-shaped front longitudinal beam provided by the present invention;

[0072] Figure 15 This is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0073] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0074] Before presenting the embodiments, the following terms are explained.

[0075] Evolutionary algorithms are designed based on the evolutionary laws of organisms in nature. They are computational models of biological evolution that simulate the natural selection and genetic mechanisms of Darwin's theory of evolution. They are a method for searching for optimal solutions by simulating the natural evolutionary process. Through mathematical methods and computer simulation, these algorithms transform the problem-solving process into a process similar to the crossover and mutation of chromosome genes in biological evolution. When solving complex combinatorial optimization problems, they can typically achieve better results faster than conventional optimization algorithms.

[0076] Example 1:

[0077] The present invention provides a method, device and electronic equipment for designing parameters of an S-shaped front longitudinal beam, which are described below respectively.

[0078] Figure 1 A flow chart of an embodiment of a method for designing parameters of an S-shaped front longitudinal beam provided by the present invention is shown as follows: Figure 1 As shown in FIG, the design method of the S-type front longitudinal beam parameters includes:

[0079] S101: Construct a target model of the S-shaped front longitudinal beam, generate a direction vector in the target space, and generate a random initial population in the decision space;

[0080] S102: obtaining a first population and a temporary population based on the initial population and the direction vector;

[0081] S103: Grid-dividing the first population to obtain first grid data of the first population; S104: Performing a selection operation on the first population based on the temporary population according to the probability table to generate a second population;

[0082] S105: Grid-dividing the second population to obtain second grid data of the second population;

[0083] S106: updating the probability table based on the first grid data and the second grid data, and the updated probability table is used for the selection operation in S104 in the next iteration step;

[0084] S107: merging the first population and the second population to obtain a third population, and performing environmental selection on the third population to obtain a fourth population;

[0085] S108: Repeat steps S102 to S107 using the fourth population as the initial population, and determine the optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

[0086] Compared with the prior art, the present embodiment provides a method for designing parameters of an S-shaped front longitudinal beam, which includes: constructing and generating an initial population in a decision space and a direction vector in a target space based on a target model of the S-shaped front longitudinal beam, obtaining a first population and a temporary population based on the initial population and the direction vector; gridding the first population to obtain first grid data of the first population, thereby better evaluating the convergence and distribution of the solution set, performing a selection operation on the first population based on the temporary population according to a probability table to generate a second population, thereby selecting the most appropriate offspring generation strategy for individuals according to different evolutionary states, and dynamic feedback dominated by the grid state. The reward strategy provides dynamic feedback rewards based on changes in the grid state transition dominance relationship between the parent generation, i.e., individuals of the first population, and the offspring generation, i.e., individuals of the second population, thereby influencing the preference for the generation strategy. The second population is gridded to obtain second grid data for the second population. A probability table is updated based on the first grid data and the second grid data. The updated probability table is used for the selection operation in the next iteration step. The first population and the second population are merged to obtain a third population. The third population is subjected to environmental selection to obtain a fourth population. The fourth population is used as the initial population for iteration, and the optimal parameters of the S-shaped front longitudinal beam are determined based on the iterated population. The present invention provides dynamic feedback rewards based on changes in the grid state transition dominance relationship between the parent generation, i.e., individuals of the first population, and the offspring generation, i.e., individuals of the second population, thereby influencing the preference for the generation strategy. This improves the convergence accuracy of the algorithm and further improves the accuracy of the parameters of the S-shaped front longitudinal beam.

[0087] It should be noted that the present invention is applicable to the parameter design of the S-shaped front longitudinal beam. It can be understood that in other multi-objective optimization problems, adopting the technical solution of the present invention only requires changing the target model.

[0088] In a specific embodiment of the present invention, Figure 2 As shown in the figure, this crash box is a thin-walled structure whose properties are greatly affected by its wall thickness. The maximum crushing force and mass of this thin-walled material are limited to below a certain value. The front end of the S-shaped front longitudinal beam is connected to the crash box. In the event of a head-on collision, the front longitudinal beam deforms to absorb the energy of the collision to protect the driver and passengers in the vehicle. Its crashworthiness is closely related to the design parameters. The cross-sectional dimensions of the thin-walled structure are the main factors affecting its energy absorption performance and maximum impact resistance, such as Figure 3 As shown, its width w ,high h and thickness 𝑡 are three important parameters to be considered in the scheme design. Therefore, in order to improve the crashworthiness of the S-shaped front longitudinal beam, its scheme design can be abstracted as a dual-objective optimization problem, that is, to find a set of ( w , h ,t) , in order to maximize energy absorption while minimizing impact forces .

[0089] The S-shaped front longitudinal beam design optimization problem can be modeled as the following dual-objective optimization problem, that is, the expression of the objective model:

[0090] (1)

[0091] ,

[0092] (2)

[0093] ;

[0094] Where, ;

[0095] (3) ; represents the objective function of the target model; It means that the S-shaped front longitudinal beam maximizes energy absorption; It means that the S-shaped front longitudinal beam minimizes the impact force; Indicates the height of the S-shaped front longitudinal beam; Indicates the width of the S-shaped front longitudinal beam; Indicates the thickness of the S-shaped front longitudinal member.

[0096] In some embodiments of the present invention, constructing a target model of the S-shaped front longitudinal beam, generating a direction vector in the target space, and generating a random initial population in the decision space includes:

[0097] Construct a target model of the S-shaped front longitudinal beam to maximize energy absorption, minimize impact force, and define the width, height, and thickness of the S-shaped front longitudinal beam;

[0098] According to the range of decision variables of the target model, random initialization is performed in the decision space to obtain the initial population;

[0099] Generates uniformly distributed direction vectors in target space.

[0100] In a specific embodiment of the present invention, the parameters are first initialized. Specifically, an initial population is randomly generated in the decision space, and then their target values ​​are calculated. :

[0101] (4)

[0102] is the population size, is the number of subproblems (i.e., subpopulations), is the size of the subpopulation.

[0103] Generated in target space Evenly distributed direction vectors divide the target space into multiple subspaces. For an optimization problem with m objectives, assuming that each objective is evenly divided into H parts, each subproblem will correspond to an m-dimensional vector , where each element of the vector is taken from , and the sum of all dimensions is 1, that is, = 1. The number of vectors is ,like Figure 4 As shown in Figure 1, for a two-target problem, when the number of divisions of each target is 3, we can get 4 direction vectors in the figure. Each direction vector corresponds to a subspace, and the vector coordinates are shown in Table 1.

[0104] Table 1: Direction vector examples

[0105]

[0106] Then, according to the angle between the individual and the direction vector, the individual is associated with the direction vector with the smallest angle and assigned to the corresponding subspace. The individuals in the same subspace form the corresponding subpopulation. Because the population and direction vectors are uniformly distributed during initialization, the size of the obtained subpopulation is also relatively average. Figure 4 As shown, according to the direction vector v 1. v 2. v 3. v 4. Divide the space into 4 areas Ω 1. Ω 2. Ω 3. Ω 4. Calculate individual x i With direction vector v j Angle θ ij ,like , attach the individual to the vector v k On. Figure 4 middle x 1 attached to the direction vector v 1， Divided into regions Ω 1 in; x 2 attached to the direction vector v 2， Divided into regions Ω 2 in…

[0107] (5)

[0108] The above describes the process of population initialization. After the population initialization is completed, the population will enter the iterative evolution stage.

[0109] In some embodiments of the present invention, in step S102, as Figure 5 As shown, the first population and the temporary population are obtained based on the initial population and the direction vector, including:

[0110] S501, determining the angle between the individuals in the initial population and the direction vector;

[0111] S502: Associating individuals in the initial population with the direction vector with the smallest angle to obtain a plurality of first associated subpopulations of the initial population;

[0112] S503: Determine a first population and a temporary population based on the number of the first associated subpopulations and the preset number of subpopulations of the initial population.

[0113] In some embodiments of the present invention, determining the first population and the temporary population based on the number of the first associated subpopulation and the preset number of subpopulations of the initial population includes:

[0114] When it is determined that the number of the first associated subpopulations is less than a preset number of subpopulations of the initial population, a preset number of subpopulations is selected from the initial population and added to the first associated subpopulation to obtain a first population and a temporary population, where the preset number of subpopulations is the preset number of subpopulations of the initial population minus the number of the first associated subpopulations;

[0115] After determining that the number of the associated subpopulations is not less than the preset number of subpopulations of the initial population, a first population and a temporary population are obtained based on a non-dominated sorting algorithm.

[0116] In a specific embodiment of the present invention, the determining of the first population and the temporary population based on the number of the first associated subpopulation and the preset number of subpopulations of the initial population includes the following specific steps:

[0117] Step 1: Initialize the angle matrix θ =zeros( N , K ), calculate each individual in the first population P according to formula (5) i With each direction vector k Angle θ ik ;

[0118] Step 2: Find θ The minimum subscript of the i-th row in the matrix , then the individual i With direction vectorj Related, specific examples are Figure 4 As shown. At the same time, individual i is assigned to the subpopulation p j .

[0119] Step 3: If , then randomly select from the first population P Individuals are added to the subpopulation middle;

[0120] like , then the subpopulation p j Perform non-dominated sorting. Non-dominated sorting method: First, individuals that are not dominated by any individual are placed in the first layer of the non-dominated sorting and stored in ; Then remove the individuals in the first layer, and among the remaining individuals, the non-dominated individuals are placed in the second layer and stored ; and so on, until the number of individuals that have undergone non-dominated sorting exceeds In the last layer of non-dominated sorting, calculate the nearest neighbor distances between individuals in the last layer and sort them in descending order, truncating the ones at the back. individual;

[0121] Step 4: Traverse each individual in the first population P and find the direction vector associated with the individual j and the corresponding subpopulation ,exist Another individual is randomly selected as the parent and saved in the temporary population P'.

[0122] In some embodiments of the present invention, in step S103, as Figure 6 As shown, the first grid data includes a grid state and a grid dominance ranking, and the first grid data of the first population obtained by performing grid division on the first population includes:

[0123] S601: Determine the upper boundary and the lower boundary of the first target space based on the preset dimension and the target model;

[0124] S602: Divide the target model into a preset number of parts to obtain a number of equally divided first target models;

[0125] S603, determining the coordinates of each individual in the first population based on the upper boundary and the lower boundary;

[0126] S604: Determine a grid state of the first population based on the coordinates of each individual in the first population;

[0127] S605: Determine a grid dominance ranking of the first population based on the grid status and the preset number of shares of the first population.

[0128] In a specific embodiment of the present invention, before describing in detail how to divide the first population into grids, the grid division strategy of the present technical solution is first introduced.

[0129] The grid division strategy is as follows: First, determine the j The lower and upper bounds of the dimensional boundary. Assume Represents the individuals in the population in the j The minimum function value and the maximum function value on the target are divided into div parts. j The upper and lower bounds of the target are set as:

[0130] (6)

[0131] (7)

[0132] The entire target space will be divided into div m Hyperboxes, the first j The side length of a dimension can be expressed as:

[0133] (8)

[0134] From formulas (6) and (7), we can see that lb j and ub j respectively The purpose of this design is to ensure that the boundary points do not fall on the edge of the grid. Figure 7 As shown in j Schematic diagram of the determination and division of the grid boundary on the target, the individual x in the group j dimensional grid state coordinates by lb j and ub j Determined, it can be expressed as:

[0135] (9)

[0136] In the formula Represents an individual In the The function value on the target, For individuals In the The grid coordinates on the target.

[0137] Determine the grid status of the individual: After the grid division is completed, the grid status of the individual is calculated based on the coordinates of the individual in the grid distribution. Figure 8 As shown, taking a two-target problem as an example, set div to 10, so the target space is divided into 10 2 grids, and the grid coordinates of individual x are between (0,0) and (9,9). Then, individual x Grid status s x It can be defined as:

[0138] (10)

[0139] in, For individuals x grid coordinates in the first target dimension, For individuals x The grid coordinates in the second target dimension have the following correspondence: Figure 8 As shown, the number in each small square in the figure represents the grid status of the individual divided into the corresponding grid.

[0140] Grid state dominance ranking: In order to better evaluate the convergence of the solution set, the grid dominance ranking of the individual is determined according to the grid state. The grid state dominance scheme designed in this paper is as follows:

[0141] (11)

[0142] in, s x is an individual x The grid state, div is the number of divisions of each target dimension, rank x is an individual x The grid is dominant in the ranking. For solving the minimization problem, the smaller the rank value, the more dominant the individual is. For example, in Figure 8 In the example, for individuals with grid states 2 and 11, their dominance ranking values ​​calculated by formula (11) are both 2, indicating that individuals with grid states 2 and 11 do not dominate each other under the grid partitioning. Through the coarse-grained grid partitioning method proposed in this invention, it can be seen that individuals in the grids passed by the same dotted line have the same rank. This strategy can quickly calculate the dominance relationship between individuals.

[0143] After introducing the grid division strategy, the following describes in detail how to perform grid division on the first population in this embodiment to obtain the first grid data of the first population. The specific steps are as follows:

[0144] Step 1: Divide the target space into grids according to formulas (6) and (7). The size is set to 10, so if Figure 8 As shown, the target space is divided into 100 state blocks.

[0145] Step 2: Traverse each individual of the parent generation, i.e. the first population P, and calculate the coordinates of each individual in the grid according to formulas (8) and (9). Determine the grid state of each parent generation according to formula (10) and record it as , according to formula (11), the grid state dominance ranking p of each parent individual is determined rank .

[0146] In a specific embodiment of the present invention, in step S104, a selection operation is performed on the first population based on the temporary population according to the probability table to generate a second population. Specifically, an operator selection operation is performed on the first population based on the temporary population according to the probability table to generate the second population from the genetic operator pool. First, the offspring generation strategy of this embodiment is described. The genetic operator strategy pool constructed in this embodiment includes a simulated binary crossover operator (SBX), a differential evolution operator (DE), and an LLX operator. A genetic operator strategy pool is constructed.

[0147] In each state, the parent individual selects a mutation strategy from the mutation strategy pool according to the mutation probability table to generate a child individual. The child individual compares the state dominance with that of the parent individual, performs forward feedback, gives rewards, and updates the mutation probability table, thereby adaptively adjusting the frequency of use of each strategy according to the current evolutionary state.

[0148] Construct mutation probability table: There are three mutation operators in the strategy pool, such as Figure 8 As shown in Table 2, the target space is divided into 100 grid states. Individuals in each state may select one of the genetic operators to produce offspring with a certain probability. Therefore, the probability of selecting a certain operator in a certain state is recorded in the table, which is a 100×3 matrix, as shown in Table 2. Q ( i , m j ) indicates that the state i Next Select j Probability of operators. In the initial stage of the evolutionary algorithm, each value in the matrix is ​​initialized to 1 / 3, indicating that the selection probability of each operator in each state is equal.

[0149] Table 2: Genetic operation selection probability table

[0150]

[0151] The roulette wheel algorithm is used to select the mutation operator from the strategy pool according to the mutation probability table, and then the parent individual uses the mutation operator to generate the offspring individual.

[0152] The reward feedback value reward is obtained based on the grid status and dominant ranking of the parent, i.e., the first population individual, and the offspring, i.e., the second population individual. Assume that individual a chooses the mutation strategy m i Produced offspring b , calculate the grid state of the two individuals according to formula (10), and a The grid state is recorded as s a ,individual b The grid state is recorded as s b According to formula (11), calculate the parent generation, i.e. the first population individual a and generate offspring, the second population of individuals b Grid dominance ranking ( rank a 、 rank b ), if the offspring, i.e., the second population, has a better ranking than the parent, i.e., the first population, ( rank b < rank a ), indicating the selected mutation strategy m i exist s a state produces better offspring, then the state s a Use mutation strategy m i The reward value reward is set to 1; on the contrary, if the parent, i.e., the first population, has a better ranking than the offspring, i.e., the second population, the individual ranking is better ( rank a < rank b ) or the ranking of the parent, i.e., the first population, is equal to the ranking of the offspring, i.e., the second population, rank a = rank b ), indicating that s a Select strategy m in state i If no individual with better convergence is generated, the reward value under this change is set to 0. The reward value setting is shown in formula (12):

[0153] (12)

[0154] Update the mutation probability table based on the parent generation, i.e. the individual grid state of the first population s a and the offspring, i.e. the second population individual grid state s bThe probability of using the mutation strategy to update the corresponding reward value is as follows:

[0155]

[0156] in, is the return rate, indicating that the parent state is the first population state s a The sub-state is the second population state s b level of study; is the discount factor, The bigger it is, the stronger the development capability is. The smaller the value, the stronger the exploration ability; m i The strategy is selected. The strategy is executed according to the parent state, that is, the first population state. m i Probability , the reward value from the parent state (i.e., the first population state) to the child state (i.e., the second population state) and the maximum probability max(Q(s b ,:)) to update s a State selection strategy m i The probability Q(s a ,m i ), which is used in the next roulette wheel selection algorithm. After traversing all individuals in the population, the newly generated offspring set O is obtained, which is the second population.

[0157] The specific steps in the code are as follows:

[0158] Step 1: Traverse the first population P Each individual i , according to the individual grid status , let s = . Read the evolution operation selection probability table Q s OK, that is s The execution probability of each strategy under the state is the first population individual P i Select the genetic operation to be performed. The other parent participating in the genetic operation is taken from the corresponding subscript in the temporary population P', that is, The specific method is as follows:

[0159] Generate a random number rand ∈[0,1)

[0160] CP=0

[0161] for j=1 to 3

[0162] CP=CP+Q sj

[0163] if rand <CP

[0164] Strategy i =j

[0165] Break;

[0166] end_if

[0167] end_for

[0168] Step 2: Record the selected strategy according to strategy and select the genetic operation operator for individual i.

[0169] switch (strategy i )

[0170] Case 1: / / Execute SBX genetic operator operation

[0171] if rand≤0.5

[0172]

[0173] else

[0174]

[0175] end_if

[0176] break

[0177] Case 2: / / Execute DE genetic operator operation

[0178]

[0179] break

[0180] Default: / / Execute LLX genetic operator operation

[0181] / / t and T represent the current evolutionary generation and the maximum evolutionary generation respectively

[0182]

[0183]

[0184] Break

[0185] end_switch

[0186] Step 3: Calculate the status and ranking of each individual in the offspring population O according to formulas (8), (9), (10), and (11), and record them in the array , O rank ;

[0187] Step 4: Update the genetic operation selection probability table Q. Compare the status ranking changes of the offspring, i.e., individuals in the second population, and the parent, i.e., individuals in the first population, in turn. Calculate the reward value according to formula (12). Formula (13) updates the probability that the parent, i.e., individuals in the first population, will use this strategy in this state next time. The specific operations are as follows:

[0188] for i=1 to NP

[0189] if <

[0190] j= Strategy i

[0191] reword=1

[0192]

[0193]

[0194] end_if

[0195] end_for

[0196] In some embodiments of the present invention, updating the probability table based on the first grid data and the second grid data includes:

[0197] The first individual in the first population produces a second individual as an offspring based on the strategy in the probability table, and the second individual is an individual in the second population;

[0198] Calculate the mesh states of the first body and the second body;

[0199] determining a grid dominance ranking of the first individual based on the grid state of the first individual;

[0200] determining a grid dominance ranking of the second individual based on the grid state of the second individual;

[0201] When it is determined that the grid dominance ranking of the second individual is lower than the grid dominance ranking of the first individual, determining the reward value to be a first preset value;

[0202] When it is determined that the grid dominance ranking of the second individual is not less than the grid dominance ranking of the first individual, determining the reward value to be a second preset value;

[0203] The probability value of the strategy in the probability table is determined based on the reward value and the current probability value of the strategy.

[0204] In some embodiments of the present invention, performing environmental selection on the third population to obtain the fourth population comprises:

[0205] Calculating the angle between each individual in the third population and the direction vector;

[0206] Associating individuals in the third population with the direction vector with the smallest angle to obtain a plurality of second associated subpopulations of the third population;

[0207] When it is determined that the number of the second associated subpopulations is less than the preset number of subpopulations of the initial population, a preset number of subpopulations is selected from the three populations and added to the second associated subpopulation, where the preset number of subpopulations is the preset number of subpopulations of the initial population minus the number of the second associated subpopulations;

[0208] After determining that the number of the second associated subpopulations is not less than the preset number of subpopulations of the initial population, a fourth population is obtained based on the angle and grid dominance ranking of each individual of the third population.

[0209] In a specific embodiment of the present invention, a third population is obtained by merging, and a fourth population is obtained by performing environmental selection on the grid and direction vector angle dual-standard selection mechanism of the third population. This dual-standard selection maintains both the convergence of the population and the distribution of the population. The specific steps are as follows:

[0210] Step 1: Set each subpopulation , initialize the angle matrix θ =zeros(2 N , K ), calculate each individual in U according to formula (5) i With each direction vector k Angle θ ik .

[0211] Step 2: Find out θ The first i Minimum row subscript , then the individual i With direction vector j Related, specific examples are Figure 3 As shown. i Assigned to subpopulations p j .

[0212] Step 3: If , then from outside the subpopulation Set selection and direction vector vj The front with the smallest angle Individuals join the subpopulation , and Join the fourth group; if , then the subpopulation p j The individuals in the grid are ranked by grid dominance and direction vector angle. Set SP = ,Temp= . SP=SP∪Temp, the subpopulation p j All individuals with the smallest grid dominance ranking are stored in Temp and extracted from p j , and repeat the above steps until |SP|+|Temp|≥ . Use formula (5) to calculate the individual and direction vectors in Temp v j The smallest angle Individuals join SP. p j =SP and p j Join the fourth group.

[0213] Step 4: Iteration = Iteration + 1. Use the fourth population obtained as the initial population for the next iteration until Iteration > Max_Iteration. Output the population P after the last iteration as the optimal solution obtained by the algorithm.

[0214] The embodiment of the present invention performs environmental selection on the merged population based on a dual selection mechanism of direction vector partitioning and state grid dominance. This dual selection mechanism ensures the convergence of the population while maintaining the diversity of the population.

[0215] Example 2: ∅

[0216] like Figure 9 The specific steps of Example 2 are shown below:

[0217] Step 1: Initialize parameters and the number of problems to be optimized m 2, set the number of direction vectors K =10, population size N =100, the number of divisions in each dimension of the target space , the maximum number of iterations Max_Iteration=20000, the initial probability of each item in the mutation probability table is 1 / 3, the learning rate , discount factor .

[0218] Step 2: Randomly initialize the population in the decision space P ={ x 1, x 2, …, x N},in x i =( x i1 , x i2 , x i3 ),make x i1 ∈[136,146], x i2 ∈[58,66], x i3 ∈[14,22].

[0219] Step 3: Generate 10 uniformly distributed direction vectors in the target space, as shown in Table 3 and Figure 10 shown.

[0220] Table 3: Direction vectors

[0221]

[0222] Step 4: Associate the individual with the direction vector with the smallest angle, and form a subpopulation of individuals in the same sub-region , so that the size of each subpopulation is .

[0223] Step 4.1: Initialize the angle matrix θ =zeros( N , K ), calculate each individual in P according to formula (5) i With each direction vector k Angle θ ik .

[0224] Step 4.2: Find out θ The minimum subscript of the i-th row in the matrix , then the individual i With direction vector j Related, specific examples are Figure 3 As shown. At the same time, individual i is assigned to the subpopulation p j .

[0225] Step 4.3: If , then randomly select from the population P Individuals are added to the subpopulation middle;

[0226] like , then the subpopulation p j Perform non-dominated sorting. Non-dominated sorting method: First, individuals that are not dominated by any individual are placed in the first layer of the non-dominated sorting and stored in the temporary population. ; Then remove the individuals in the first layer, and among the remaining individuals, the non-dominated individuals are placed in the second layer and stored ; and so on, until the number of individuals that have been non-dominated exceeds In the last layer of non-dominated sorting, calculate the nearest neighbor distances between individuals in the last layer and sort them in descending order, truncating the ones at the back. Individuals.

[0227] Step 5: Traverse each individual in the first population P and find the direction vector associated with the individual j and the corresponding first associated subpopulation ,exist Another individual is randomly selected as the parent and saved in the temporary population P'.

[0228] Step 6: Divide the grid and determine the grid status of the individuals in the parent generation, i.e. the first population.

[0229] Step 6.1: Divide the target space into grids according to formulas (6) and (7). The size is set to 10, so if Figure 8 As shown, the target space is divided into 100 state blocks.

[0230] Step 6.2: Traverse each individual of the parent generation, i.e., the first population, and calculate the coordinates of each individual in the grid according to formulas (8) and (9). Determine the grid status of each parent generation, i.e., the first population, according to formula (10) and record it as , according to formula (11), determine the grid state dominance ranking p of each parent, i.e., the first population individual rank .

[0231] Step 7: Figure 11 As shown, it is the parent generation, that is, the first population P Each individual in i Select genetic operation operators in the operator pool to generate offspring, namely the second population O.

[0232] Step 7.1: Traverse the parent population, i.e. the first population P Each individual i , according to the individual state calculated in step 6 , let s = . Read the genetic operation selection probability table Q s OK, that is s The execution probability of each strategy in the state is the parent, that is, the first population individual P i Select the genetic operation to be performed. The other parent participating in the genetic operation is taken from the corresponding subscript in the temporary population P', that is, The specific method is as follows:

[0233] Generate a random number rand ∈[0,1)

[0234] CP=0

[0235] for j=1 to 3

[0236] CP=CP+Q sj

[0237] if rand <CP

[0238] Strategy i =j

[0239] Break;

[0240] end_if

[0241] end_for

[0242] Step 7.2: Record the selected strategy according to strategy and select the genetic operation operator for the first population individual i.

[0243] switch (strategy i )

[0244] Case 1: / / Execute SBX genetic operator operation

[0245] if rand≤0.5

[0246]

[0247] else

[0248]

[0249] end_if

[0250] break

[0251] Case 2: / / Execute DE genetic operator operation

[0252]

[0253] break

[0254] Default: / / Execute LLX genetic operator operation

[0255] / / t and T represent the current evolutionary generation and the maximum evolutionary generation respectively

[0256]

[0257]

[0258] Break

[0259] end_switch

[0260] Step 8: Calculate the status and ranking of each individual in the offspring population, i.e., the second population O, according to formulas (8), (9), (10), and (11), and record them in the array , O rank .

[0261] Step 9: Update the genetic operation selection probability table Q. Compare the status ranking changes of the offspring individuals, i.e., individuals in the second population, and the parent individuals, i.e., individuals in the first population, in turn. Calculate the reward value according to formula (12). Formula (13) updates the probability that the parent individual, i.e., individuals in the first population, will use this strategy in this state next time. The specific operations are as follows:

[0262] for i=1 to NP

[0263] if <

[0264] j= Strategy i

[0265] reword=1

[0266]

[0267]

[0268] end_if

[0269] end_for

[0270] Step 10: Merge the parent population, i.e., the first population P, and the offspring population, i.e., the second population O, into a set U, i.e., the third population. Perform environmental selection using the dual-standard selection mechanism of grid and direction vector angle designed in this scheme to obtain the fourth population. This dual-standard selection maintains both the convergence of the population and the distribution of the population.

[0271] Step 10.1: Set each subpopulation , initialize the angle matrix θ =zeros(2 N , K ), calculate each individual in U according to formula (5) i With each direction vector k Angle θ ik .

[0272] Step 10.2: Find out θ The first i Minimum row subscript , then the individual i With direction vector j Related, specific examples are Figure 3 As shown. i Assigned to subpopulations p j .

[0273] Step 10.3: If , then from outside the subpopulation Set selection and direction vector v j The front with the smallest angle Individuals join the subpopulation P j , and P j Join the fourth group; if , then the subpopulation p j The individuals in the grid are ranked by grid dominance and direction vector angle. Set SP = ,Temp= . SP=SP∪Temp, the subpopulation p j All individuals with the smallest grid dominance ranking are stored in Temp and extracted from p j , and repeat the above steps until |SP|+|Temp|≥ . Use formula (5) to calculate the individual and direction vectors in Temp v j The smallest angle Individuals join SP. p j =SP, and p j Join the fourth population. The specific selection process is as follows Figure 12 shown.

[0274] Step 11: Iteration = Iteration + 1. Go to step 5 and use the fourth population as the initial population for the next iteration until Iteration > Max_Iteration. Output the population after the last iteration as the optimal solution obtained by the algorithm.

[0275] Experimental results: According to the above specific implementation, the algorithm is used to solve the S-shaped front longitudinal beam design optimization problem and conduct simulation experiments. In 30 independent runs of the experiment, the population size N = 100, the maximum number of iterations Max_Iteration = 20000, the number of direction vectors K = 10, and the learning rate , discount factor .

[0276] Experiments were conducted using the present invention. Figure 13 The visualization data of a set of Pareto solutions in the target space in 30 independent experiments is presented. It can be seen that the optimal solution obtained by the algorithm proposed in this invention approaches the Pareto frontier and the distribution between solutions is also good.

[0277] A set of experimental results from 30 experiments was screened, and two individuals from the obtained non-dominated solutions were extracted for comparison with the solutions designed by the prior art, as shown in Table 4. The brackets in Table 4 indicate the percentage improvement of the solution of the present invention compared with the original solution, which is calculated using Formula (14).

[0278] (14)

[0279] Table 4 Comparison of algorithm solution results

[0280]

[0281] Experimental results show that compared with the original design, in the event of a frontal collision, Result 1 improves energy absorption by 0.47% and reduces impact force by 1.44%. Result 2 improves energy absorption by 0.76% and reduces impact force by 0.62%.

[0282] In order to better implement a method for designing parameters of an S-shaped front longitudinal beam in an embodiment of the present invention, based on a method for designing parameters of an S-shaped front longitudinal beam, correspondingly, Figure 14 As shown, an embodiment of the present invention further provides a device for designing parameters of an S-shaped front longitudinal beam. The device 1400 for designing parameters of an S-shaped front longitudinal beam includes:

[0283] Initialization parameter module 1401 is used to construct a target model of the S-shaped front longitudinal beam, generate a direction vector in the target space, and generate a random initial population in the decision space;

[0284] A first population acquisition module 1402 is configured to obtain a first population and a temporary population based on the initial population and the direction vector;

[0285] The first grid data acquisition module 1403 is used to perform grid division on the first population to obtain first grid data of the first population; the second population acquisition module 1404 is used to select the first population based on the temporary population according to the probability table to generate the second population;

[0286] The second grid data acquisition module 1405 is used to perform grid division on the second population to obtain second grid data of the second population;

[0287] A probability table updating module 1406 is configured to update the probability table based on the first grid data and the second grid data, and the updated probability table is used for the selection operation in the next iteration step;

[0288] The population merging module 1407 is used to merge the first population and the second population to obtain a third population, and perform environmental selection on the third population to obtain a fourth population;

[0289] The loop iteration module 1408 is configured to repeat steps S2 to S7 using the fourth population as the initial population, and determine optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

[0290] The device 1400 for designing parameters of an S-shaped front longitudinal beam provided in the above embodiment can implement the technical solution described in the embodiment of the method for designing parameters of an S-shaped front longitudinal beam. The specific implementation principles of the above modules or units can be found in the corresponding contents in the embodiment of the method for designing parameters of an S-shaped front longitudinal beam, which will not be repeated here.

[0291] like Figure 15 As shown, the present invention also provides an electronic device 1500. The electronic device 300 includes a processor 1501, a memory 1502 and a display 1503. Figure 15 Only some of the components of the electronic device 300 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0292] In some embodiments, the processor 1501 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 1502 , such as a method for designing parameters of an S-shaped front longitudinal beam in the present invention.

[0293] In some embodiments, processor 1501 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 1501 may be local or remote. In some embodiments, processor 1501 may be implemented on a cloud platform. In some embodiments, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.

[0294] In some embodiments, the memory 1502 may be an internal storage unit of the electronic device 1500, such as a hard disk or memory of the electronic device 1500. In other embodiments, the memory 1502 may also be an external storage device of the electronic device 1500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1500.

[0295] Furthermore, the memory 1502 may include both an internal storage unit of the electronic device 1500 and an external storage device. The memory 1502 is used to store application software installed in the electronic device 1500 and various data.

[0296] In some embodiments, display 1503 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 1503 is used to display information on electronic device 1500 and to display a visual user interface. Components 1501-1503 of electronic device 1500 communicate with each other via a system bus.

[0297] In one embodiment, when the processor 1501 executes a design program for parameters of an S-shaped front longitudinal beam in the memory 1502, the following steps may be implemented:

[0298] S1: Construct a target model of the S-shaped front longitudinal beam, generate a direction vector in the target space, and generate a random initial population in the decision space;

[0299] S2: Get the first population and temporary population based on the initial population and direction vector;

[0300] S3: gridding the first population to obtain first grid data of the first population; S4: performing a selection operation on the first population based on the temporary population according to the probability table to generate a second population;

[0301] S5: performing grid division on the second population to obtain second grid data of the second population;

[0302] S6: updating the probability table based on the first grid data and the second grid data, and the updated probability table is used for the selection operation in S4 in the next iteration step;

[0303] S7: The first and second populations are combined to obtain a third population, and the third population is subjected to environmental selection to obtain a fourth population;

[0304] S8: Repeat steps S2-S7 with the fourth population as the initial population, and determine the optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

[0305] It should be understood that, when the processor 1501 executes a design program for parameters of an S-shaped front longitudinal beam in the memory 1502 , in addition to the above functions, other functions may also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0306] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 1500 mentioned. The electronic device 1500 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1500 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0307] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0308] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for designing parameters of an S-shaped front longitudinal beam, characterized in that: include: S1: Construct a target model of the S-shaped front longitudinal beam, generate a direction vector in the target space, and generate a random initial population in the decision space; S2: Get the first population and temporary population based on the initial population and direction vector; S3: gridding the first population to obtain first grid data of the first population; S4: performing a selection operation on the first population based on the temporary population according to the probability table to generate a second population; S5: performing grid division on the second population to obtain second grid data of the second population; S6: updating the probability table based on the first grid data and the second grid data, and the updated probability table is used for the selection operation in S4 in the next iteration step; S7: The first and second populations are combined to obtain a third population, and the third population is subjected to environmental selection to obtain a fourth population; S8: Repeat steps S2-S7 with the fourth population as the initial population, and determine the optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

2. The method for designing parameters of an S-shaped front longitudinal beam according to claim 1, characterized in that: The target model of the S-shaped front longitudinal beam is constructed, a direction vector is generated in the target space, and a random initial population is generated in the decision space, including: Construct a target model of the S-shaped front longitudinal beam to maximize energy absorption, minimize impact force, and define the width, height, and thickness of the S-shaped front longitudinal beam; According to the range of decision variables of the target model, random initialization is performed in the decision space to obtain the initial population; Generates uniformly distributed direction vectors in target space.

3. The method for designing parameters of an S-shaped front longitudinal beam according to claim 1, characterized in that: The step of obtaining the first population and the temporary population based on the initial population and the direction vector includes: Determine the angle between individuals in the initial population and the direction vector; Associating individuals in the initial population with the direction vector with the smallest angle to obtain a plurality of first associated subpopulations of the initial population; The first population and the temporary population are determined based on the number of the first associated subpopulations and the preset number of subpopulations of the initial population.

4. The method for designing parameters of an S-shaped front longitudinal beam according to claim 3, characterized in that: The determining of the first population and the temporary population based on the number of the first associated subpopulations and the preset number of subpopulations of the initial population includes: When it is determined that the number of the first associated subpopulations is less than a preset number of subpopulations of the initial population, a preset number of subpopulations is selected from the initial population and added to the first associated subpopulation to obtain a first population and a temporary population, where the preset number of subpopulations is the preset number of subpopulations of the initial population minus the number of the first associated subpopulations; After determining that the number of the associated subpopulations is not less than the preset number of subpopulations of the initial population, a first population and a temporary population are obtained based on a non-dominated sorting algorithm.

5. The method for designing parameters of an S-shaped front longitudinal beam according to claim 1, characterized in that: The first grid data includes a grid state and a grid dominance ranking. The first grid data of the first population obtained by gridding the first population includes: Determining an upper boundary and a lower boundary of a first target space based on the preset dimensions and the target model; Divide the target model into a preset number of parts to obtain a number of equally divided first target models; determining the coordinates of each individual in the first population based on the upper boundary and the lower boundary; determining a grid state of the first population based on the coordinates of each individual in the first population; A grid dominance ranking of the first population is determined based on the grid status of the first population and a preset number of shares.

6. The method for designing parameters of an S-shaped front longitudinal beam according to claim 1, characterized in that: The updating of the probability table based on the first grid data and the second grid data includes: The first individual in the first population produces a second individual as an offspring based on the strategy in the probability table, and the second individual is an individual in the second population; Calculate the mesh states of the first body and the second body; determining a grid dominance ranking of the first individual based on the grid state of the first individual; determining a grid dominance ranking of the second individual based on the grid state of the second individual; When it is determined that the grid dominance ranking of the second individual is lower than the grid dominance ranking of the first individual, determining the reward value to be a first preset value; When it is determined that the grid dominance ranking of the second individual is not less than the grid dominance ranking of the first individual, determining the reward value to be a second preset value; The probability value of the strategy in the probability table is determined based on the reward value and the current probability value of the strategy.

7. The method for designing parameters of an S-shaped front longitudinal beam according to claim 2, characterized in that: The step of performing environmental selection on the third population to obtain the fourth population comprises: Calculating the angle between each individual in the third population and the direction vector; Associating individuals in the third population with the direction vector with the smallest angle to obtain a plurality of second associated subpopulations of the third population; When it is determined that the number of the second associated subpopulations is less than the preset number of subpopulations of the initial population, a preset number of subpopulations is selected from the three populations and added to the second associated subpopulation, where the preset number of subpopulations is the preset number of subpopulations of the initial population minus the number of the second associated subpopulations; After determining that the number of the second associated subpopulations is not less than the preset number of subpopulations of the initial population, a fourth population is obtained based on the angle and grid dominance ranking of each individual of the third population.

8. A design device for parameters of an S-shaped front longitudinal beam, characterized in that: include: Initialization parameter module, used to build the target model of the S-shaped front longitudinal beam, generate direction vectors in the target space, and generate random initial populations in the decision space; A first population acquisition module, used to obtain a first population and a temporary population based on an initial population and a direction vector; A first grid data acquisition module is used to perform grid division on the first population to obtain first grid data of the first population; a second population acquisition module is used to perform a selection operation on the first population based on the temporary population according to the probability table to generate a second population; A second grid data acquisition module is used to perform grid division on the second population to obtain second grid data of the second population; A probability table updating module, configured to update the probability table based on the first grid data and the second grid data, wherein the updated probability table is used for the selection operation in the next iteration step; A population merging module is used to merge the first population and the second population to obtain a third population, and perform environmental selection on the third population to obtain a fourth population; The loop iteration module is used to repeat steps S2-S7 using the fourth population as the initial population, and determine the optimal parameters of the S-shaped front longitudinal beam based on the iterated population.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for designing parameters of an S-shaped front longitudinal beam as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the method for designing parameters of an S-shaped front longitudinal beam as described in any one of claims 1 to 7.

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