Electromagnetic ultrasonic transducer rapid design method based on S-SADE algorithm

Abstract

The invention discloses an electromagnetic ultrasonic transducer rapid design method based on an S-SADE algorithm, and the method comprises the steps: constructing an electromagnetic ultrasonic transducer model and a joint simulation platform, obtaining initial sample data and various parameters, and initializing a population and a sample database; using samples in the database to construct an RBF proxy model; performing variation and crossover operation on the parent population individuals according to five variation strategies to obtain a filial population; using an RBF agent model to predict fitness values of offspring individuals, transmitting parameters of an optimal individual to an electromagnetic ultrasonic transducer model for simulation evaluation, and updating a database; selecting a most expected sub-population from the five sub-populations as a next-generation population; and outputting an optimal parameter design scheme. According to the adaptive differential evolution algorithm, appropriate variation strategies and control parameters can be selected in different evolution stages, the objective function values of population individuals are predicted through the proxy model instead of expensive simulation evaluation, and computing resources can be saved to a great extent.

Description

Technical Field

[0001] The present invention relates to the fields of electromagnetic ultrasonic transducer manufacturing and evolutionary computing, and in particular to a rapid design method for an electromagnetic ultrasonic transducer based on an S-SADE algorithm. Background Art

[0002] Electromagnetic ultrasonic transducer is a new type of electroacoustic converter based on electromagnetic ultrasonic conversion technology. It is also a non-contact transducer that integrates ultrasonic transmitter and receiver. It is widely used in the field of ultrasonic non-destructive testing. By optimizing the structure of electromagnetic ultrasonic transducer, the energy conversion efficiency and frequency response characteristics can be improved, the complexity of the manufacturing process can be reduced, and the convenience of manufacturing and assembly can be improved, providing higher reliability for the application of electromagnetic ultrasonic transducer. The structural optimization of electromagnetic ultrasonic transducer usually involves the optimization of five parameters: magnet radius, magnet height, CML coil width, lifting distance and alternating current. The optimization purpose is to maximize the signal amplitude, which belongs to a single-objective optimization problem. In addition, in the design and optimization of electromagnetic ultrasonic transducer, it is often necessary to call COMSOL Multiphysics software to perform a large number of time-consuming model simulations. The high simulation time cost seriously reduces the solution efficiency of the existing optimization method. Improving the optimization efficiency of electromagnetic ultrasonic transducer has become a task that needs to be solved urgently.

[0003] For such expensive optimization problems, due to their high computational cost, traditional evolutionary algorithms consume a lot of computing resources and are inefficient when solving such problems. Therefore, special optimization strategies and algorithms are needed to accelerate the solution process and improve the optimization solution efficiency. In recent years, researchers have proposed using evolutionary algorithms assisted by surrogate models to solve expensive optimization problems. This method combines the global search capability of evolutionary algorithms and the efficient computing advantages of surrogate models. In order to reduce the consumption of computing resources in the optimization process, the surrogate model is constructed with the solution evaluated by the real objective function, and then the surrogate model is used to predict the objective function value instead of the expensive real evaluation, which can greatly reduce the number of real evaluations, reduce time costs, and find high-quality solutions within limited computing resources. The use of surrogate model-assisted evolutionary algorithms for parameter design of electromagnetic ultrasonic transducers can improve the conversion performance of electromagnetic ultrasonic transducers and the efficiency of non-destructive testing, which has important practical significance for promoting the development of industrial automation.

[0004] In the process of implementing the present invention, the inventors found that the prior art has the following technical problems:

[0005] Although some adaptive differential evolution algorithms have been used to solve single-objective optimization problems, their potential has not yet been fully explored. On the one hand, some adaptive differential evolution algorithms do not take into account multiple mutation strategies and different control parameters at the same time, so they cannot select the most appropriate strategies and parameters at different evolution stages; on the other hand, in order to obtain higher-quality solutions in the optimization process, it is usually necessary to perform a lot of time-consuming trial-and-error searches to find the most appropriate strategies and related parameter values. This process requires a lot of function evaluations and has low optimization efficiency. In addition, in some surrogate model-assisted adaptive differential evolution algorithms, the error between the model prediction value and the true value is large. The update strategy of the surrogate model will affect the accuracy of the model prediction and thus affect the performance of the algorithm. The model update strategy involves how to update the model in the iteration of the evolutionary algorithm. The key lies in how to select sample data for training the surrogate model. If the sample is insufficient or unrepresentative, the accuracy of the model prediction may be reduced. Summary of the invention

[0006] The purpose of the present invention is to solve the above problems. A rapid design method for electromagnetic ultrasonic transducers based on the S-SADE algorithm is developed, which integrates the agent model and the adaptive differential evolution algorithm, and can efficiently solve the electromagnetic ultrasonic transducer parameter design problem.

[0007] The technical solution adopted by the present invention is: a rapid design method of an electromagnetic ultrasonic transducer based on an S-SADE algorithm, comprising the following steps:

[0008] S1, use COMSOL software to establish an electromagnetic ultrasonic transducer simulation model and build a MATLAB-COMSOL joint simulation platform;

[0009] S2, obtain sample data, initialize parameters, population and sample database;

[0010] S3, constructs the RBF proxy model using samples in the database during the evolution process;

[0011] S4, the parent population is mutated and crossovered according to five mutation strategies to obtain five offspring populations;

[0012] S5, using the proxy model to evaluate the objective function value of the offspring individual and perform a selection operation, passing the parameters of the best individual in each offspring population to the electromagnetic ultrasonic transducer model and performing simulation evaluation, and updating the database and the global best individual in the subpopulation;

[0013] S6, select the most promising one from the five offspring populations as the next generation population;

[0014] S7, determine whether the maximum number of true evaluation times has been reached; if so, stop the evolution and output the optimal parameter design solution; otherwise, go to S3 to continue the search process.

[0015] Furthermore, the specific steps of S1 include:

[0016] S11, establishing a finite element model for electromagnetic ultrasonic transducer simulation in COMSOL simulation software with multiple physical field modules, selecting materials and setting parameters, selecting and setting physical fields for the constructed model, adding excitation sources to the model and performing meshing;

[0017] S12, construct a finite element model using the AC / DC module and the magnetic field module. The structural mechanics module is used for the generation and transmission of SV waves. The finite element model consists of a CML coil, a cylindrical magnet, an aluminum plate, surrounding air, and an infinite domain.

[0018] S13, the optimization goal is to maximize the single-point signal amplitude of the designed electromagnetic ultrasonic transducer model, and the objective function can be defined as:

[0019] Max f(x)=A w,i(x)

[0020] Among them, A w,i(x) is the signal amplitude at the ith observation point, and each observation point x = [R, H, W, L, I] contains multiple optimized parameters, where R is the magnet radius, H is the magnet height, W is the CML coil width, L is the lifting distance, and I is the alternating current;

[0021] S14, build a MATLAB-COMSOL joint simulation platform to realize the functions of calling COMSOL models in Matlab, modifying optimization variable values, simulating calculations and outputting optimization targets, that is, realizing the automatic operation of the electromagnetic ultrasonic transducer parameter optimization algorithm and real-time feedback of simulation results.

[0022] Furthermore, the specific steps of S2 include:

[0023] S21, initialization population size NP, maximum number of true evaluations F max , the maximum number of iterations G m , learn algebraic LP;

[0024] S22, randomly generate 100 sets of data as initial population individuals within the parameter variation range of each optimization variable, that is, each individual corresponds to a set of optimization parameters;

[0025] S23, transmit the data of each individual in the initial population to COMSOL software through the MATLAB-COMSOL joint simulation platform, set the parameters of the electromagnetic ultrasonic transducer and run the simulation to calculate the single-point signal amplitude, output the results to Matlab, obtain 100 real samples and store them in the database;

[0026] S24, for the maximization problem, sort the initial samples from large to small according to the objective function value, select the first NP individuals with larger objective function values ​​as the initial population, and store them in the database.

[0027] Furthermore, the specific steps of S3 include:

[0028] S31, if the number of samples in the database is greater than 2NP, sort them from large to small according to the objective function value, retain the first 2NP samples, and delete the other samples;

[0029] S32, randomly selecting NP samples from the database as training data for training the RBF proxy model.

[0030] Furthermore, the specific steps of S4 include:

[0031] S41, adaptively adjust the mutation operator F, the mutation operator F controls the amplification effect of the deviation variable, in order to balance the global search ability and local development ability of the algorithm, set the mutation operator F in each generation to change dynamically according to the following formula:

[0032]

[0033] Among them, G m represents the maximum number of iterations, gen represents the current number of iterations, and F0 is a constant set to 0.4;

[0034] S42, mutation operation. The differential evolution algorithm uses mutation operation to generate mutation vectors for each individual in the current population. For each target vector x i,G , generate mutation vector v according to the following five mutation methods i,G ;

[0035] 1)DE / rand / 1,

[0036]

[0037] 2)DE / best / 1,

[0038]

[0039] 3)DE / rand-to-best / 1,

[0040]

[0041] 4)DE / best / 2,

[0042]

[0043] 5)DE / rand / 2,

[0044]

[0045] In the formula, x best,G is the best individual in the current population; the randomly selected numbers r1, r2, r3, r4, r5 are different from the target vector number i;

[0046] S43, adaptively adjust the crossover operator CR. Before the LP generation, the crossover operator CR of the k-th mutation strategy is composed of the normal distribution N(CRm k , std) generates a set of CR values ​​and assigns them to each target vector;

[0047] Among them, CRm k and std are the mean and variance respectively, and CRm is initialized k The value of is 0.5, and the value of std is 0.1;

[0048] At the same time, a CRMemory k The archive is used to store the CR values ​​of the trial vectors generated by the k-th strategy in the previous LP generation that can successfully enter the next generation. After the LP generation, CRMemory k The archive stores the CR values ​​of the LP generation before the current one; in the previous LP generation, the CR value of the kth strategy is generated by N(0.5, 0.1); in each generation after the LP generation, CRm k Set to CRMemory k The average value of the data is stored in the CR value of the k-th strategy is N(CRm k , 0.1) generated;

[0049] S44, crossover operation, in order to increase the diversity of the interference parameter vector, a binomial crossover operation is introduced after the mutation operation is completed to generate the test vector u ji,G :

[0050]

[0051] (i=1,2,…,NP; j=1,2,…,D)

[0052] Where randb(j) represents the jth estimated value of a random number generator between [0,1]; D represents the dimension of the problem; rnbr(i)∈1,2,…,D represents a randomly selected sequence.

[0053] Furthermore, the specific steps of S5 include:

[0054] S51, using the contemporary trained RBF surrogate model to predict the objective function value of the offspring individuals;

[0055] S52, selection operation, to determine the test vector u i,G Will it enter the next generation population? According to the greedy rule, the test vector u i,G With the target vector x i,G For comparison, if the test vector u i,G The objective function value of is larger, then the test vector u i,G It is retained to the next generation population and its CR value is stored in CRMemory k middle;

[0056]

[0057] S53, selecting the individuals with the largest objective function value in each offspring population as filling solutions, transmitting their values ​​to COMSOL simulation software through the MATLAB-COMSOL joint simulation platform to set parameters of the electromagnetic ultrasonic transducer, and running the simulation to calculate the single-point signal amplitude, outputting the results to Matlab and updating its objective function value;

[0058] S54, determining whether the individual is in the database; if not, adding the individual and its objective function value to the database;

[0059] S55, comparing the objective function value of the individual with the global optimal individual of the current subpopulation, if the objective function value of the individual is greater, updating the individual to the global optimal individual.

[0060] Furthermore, the specific steps of S6 include:

[0061] S61, to select the most promising population from the five sub-populations obtained in S5, calculate the average fitness value of all individuals in each sub-population, and the population with the largest average fitness value will be used as the initial population of the next generation. The calculation formula is as follows:

[0062]

[0063] S62, using the selected sub-population as the initial population of the next generation, and updating the global optimal individual in the sub-population to the global optimal individual.

[0064] The beneficial effects produced by the present invention are:

[0065] 1. The adaptive selection mutation strategy can select appropriate strategies at different stages of evolution and accelerate the convergence speed;

[0066] 2. Adaptive adjustment of mutation operators and crossover operators can balance the algorithm's global search capability and local development capability;

[0067] 3. Using the proxy model to predict the objective function value of individuals in the population can reduce the number of real simulation evaluations in the electromagnetic ultrasonic transducer parameter design, save computing resources, and improve the solution speed; BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flow chart of the algorithm of the present invention;

[0069] Figure 2 It is a structural schematic diagram of the electromagnetic ultrasonic transducer model. DETAILED DESCRIPTION

[0070] The present invention will be further described below in conjunction with the accompanying drawings.

[0071] like Figure 1 As shown, the present invention is a rapid design method of an electromagnetic ultrasonic transducer based on an S-SADE algorithm, comprising the following steps:

[0072] S1, use COMSOL software to establish an electromagnetic ultrasonic transducer simulation model and build a MATLAB-COMSOL joint simulation platform; the specific steps are:

[0073] S11, establish a finite element model for electromagnetic ultrasonic transducer simulation in COMSOL simulation software with multiple physical field modules, select materials and set parameters, select and set the physical field of the constructed model, add excitation sources to the model and perform meshing.

[0074] S12, the constructed finite element model is as follows Figure 2 As shown in the figure, the model uses the AC / DC module and the magnetic field module, and the structural mechanics module is used for the generation and transmission of SV waves. The finite element model consists of a CML coil, a cylindrical magnet, an aluminum plate, surrounding air, and an infinite domain, which acts on infinite space to ensure the stability and convergence of the solution.

[0075] S13, the optimization goal is to maximize the single-point signal amplitude of the designed electromagnetic ultrasonic transducer model, and the objective function can be defined as:

[0076] Max f(x)=A w,i(x)

[0077] Among them A w,i(x) is the signal amplitude at the i-th observation point. Each observation point x = [R, H, W, L, I] contains multiple optimized parameters, where R is the magnet radius, H is the magnet height, W is the CML coil width, L is the lifting distance, and I is the alternating current.

[0078] S14, build a MATLAB-COMSOL joint simulation platform to realize the functions of calling COMSOL models in Matlab, modifying optimization variable values, simulating calculations and outputting optimization targets, that is, realizing the automatic operation of the electromagnetic ultrasonic transducer parameter optimization algorithm and real-time feedback of simulation results.

[0079] S2, obtain sample data, initialize parameters, population and sample database; the specific steps are:

[0080] S21, initialization population size NP, maximum number of true evaluations F max , the maximum number of iterations G m , learn algebraic LP.

[0081] S22, randomly generate 100 groups of data as initial population individuals within the parameter variation range of each optimization variable, that is, each individual corresponds to a set of optimization parameters.

[0082] S23, transmit the data of each individual in the initial population to COMSOL software through the MATLAB-COMSOL joint simulation platform, set the parameters of the electromagnetic ultrasonic transducer and run the simulation to calculate the single-point signal amplitude, output the results to Matlab, obtain 100 real samples and store them in the database.

[0083] S24, for the maximization problem, sort the initial samples from large to small according to the objective function value, select the first NP individuals with larger objective function values ​​as the initial population, and store them in the database.

[0084] S3, constructing the RBF proxy model using samples in the database during the evolution process; the specific steps are:

[0085] S31, if the number of samples in the database is greater than 2NP, sort them from large to small according to the objective function value, retain the first 2NP samples, and delete the other samples.

[0086] S32, randomly selecting NP samples from the database as training data for training the RBF proxy model.

[0087] S4, the parent population is mutated and crossovered according to five mutation strategies to obtain five offspring populations; the specific steps are:

[0088] S41, adaptively adjust the mutation operator F, the mutation operator F controls the amplification effect of the deviation variable, in order to balance the global search ability and local development ability of the algorithm, set the mutation operator F in each generation to change dynamically according to the following formula:

[0089]

[0090] Among them, G m represents the maximum number of iterations, gen represents the current number of iterations, and F0 is a constant set to 0.4.

[0091] S42, mutation operation. The differential evolution algorithm uses mutation operation to generate mutation vectors for each individual in the current population. For each target vector x i,G , generate mutation vector v according to the following five mutation methods i,G :

[0092] 1)DE / rand / 1:

[0093]

[0094] 2)DE / best / 1:

[0095]

[0096] 3)DE / rand-to-best / 1:

[0097]

[0098] 4)DE / best / 2:

[0099]

[0100] 5)DE / rand / 2:

[0101]

[0102] In the formula, x best,G is the best individual in the current population; the randomly selected numbers r1, r2, r3, r4, r5 are different from the target vector number i.

[0103] S43, adaptively adjust the crossover operator CR. Before the LP generation, the crossover operator CR of the k-th mutation strategy is composed of the normal distribution N(CRm k , std) generates a set of CR values ​​and assigns them to each target vector. k and std are the mean and variance respectively, and CRm is initialized k The value of is 0.5, the value of std is 0.1; at the same time, a CRMemory is created k The archive is used to store the CR values ​​of the trial vectors generated by the k-th strategy in the previous LP generation that can successfully enter the next generation. After the LP generation, CRMemory kThe archive stores the CR values ​​of the LP generation before the current one. Specifically, in the previous LP generation, the CR value of the kth strategy is generated by N(0.5, 0.1); in each generation after the LP generation, CRm k Set to CRMemory k The average value of the data is stored in the CR value of the k-th strategy is N(CRm k , 0.1) is generated.

[0104] S44, crossover operation, in order to increase the diversity of the interference parameter vector, a binomial crossover operation is introduced after the mutation operation is completed to generate the test vector u ji,G :

[0105]

[0106] (i=1,2,…,NP; j=1,2,…,D)

[0107] Where randb(j) represents the jth estimated value of a random number generator between [0,1]; D represents the dimension of the problem; rnbr(i)∈1,2,…,D represents a randomly selected sequence.

[0108] S5, using the proxy model to evaluate the objective function value of the offspring individual and perform the selection operation, passing the parameters of the best individual in each offspring population to the electromagnetic ultrasonic transducer model and performing simulation evaluation, updating the database and the global best individual in the subpopulation; the specific steps are:

[0109] S51, using the contemporary trained RBF surrogate model to predict the objective function value of the offspring individuals;

[0110] S52, selection operation, to determine the test vector u i,G Will it enter the next generation population? According to the greedy rule, the test vector u i,G With the target vector x i,G For comparison, if the test vector u i,G The objective function value of is larger, then the test vector u i,G It is retained to the next generation population and its CR value is stored in CRMemory k middle;

[0111]

[0112] S53, select the individuals with the largest objective function value in each offspring population as filling solutions, transmit their values ​​to the COMSOL simulation software through the MATLAB-COMSOL joint simulation platform to set the parameters of the electromagnetic ultrasonic transducer, run the simulation to calculate the single-point signal amplitude, output the results to Matlab and update its objective function value.

[0113] S54, determining whether the individual is in the database; if not, adding the individual and its objective function value to the database.

[0114] S55, comparing the objective function value of the individual with the global optimal individual of the current subpopulation, if the objective function value of the individual is greater, updating the individual to the global optimal individual.

[0115] S6, select the most promising one from the five offspring populations as the next generation population; the specific steps are:

[0116] S61, to select the most promising population from the five sub-populations obtained in S5, calculate the average fitness value of all individuals in each sub-population, and the population with the largest average fitness value will be used as the initial population of the next generation. The calculation formula is as follows:

[0117]

[0118] S62, using the selected sub-population as the initial population of the next generation, and updating the global optimal individual in the sub-population to the global optimal individual.

[0119] S7, determine whether the maximum number of true evaluation times has been reached; if so, stop the evolution and output the optimal parameter design solution; otherwise, go to S3 to continue the search process.

[0120] In order to test the ability of the fusion agent model and the adaptive differential evolution algorithm S-SADE designed in this invention to solve the electromagnetic ultrasonic transducer parameter design problem, the proposed S-SADE algorithm is compared with the adaptive differential evolution algorithm SADE without using the agent model. The population size of all algorithms is set to NP = 20, and the maximum number of true evaluations is set to F max = 50. In view of the limited computing resources, all algorithms were run independently once. All algorithms were run on the MATLAB-COMSOL joint simulation platform. The simulation evaluation of the optimal individual was performed on the COMSOL software. Matlab and COMSOL communicated with each other through the API interface.

[0121] The approximate global optimal solutions and target values ​​obtained by a single run of all algorithms are shown in Table 1. The optimization goal of the electromagnetic ultrasonic transducer designed in this experiment is to maximize the single-point signal amplitude. It can be observed from the table that the optimization result of the S-SADE algorithm designed by the present invention is 1.2207e-6, and the optimization result of the SADE algorithm assisted by the non-proxy model is 1.1592e-6. The S-SADE algorithm achieves better optimization results than the SADE algorithm. In addition, in terms of time overhead, the time consumed by the S-SADE algorithm is significantly lower than that of the SADE algorithm. The comprehensive experimental results show that the S-SADE algorithm designed by the present invention can effectively handle the structural optimization design problem of the electromagnetic ultrasonic transducer.

[0122] Table 1 Experimental results of electromagnetic ultrasonic transducer parameter design

[0123]

[0124] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A rapid design method for electromagnetic ultrasonic transducers based on the S-SADE algorithm, characterized in that: The following steps are involved: S1, use COMSOL software to establish an electromagnetic ultrasonic transducer simulation model and build a MATLAB-COMSOL joint simulation platform; S2, obtain sample data, initialize parameters, population and sample database; S3, constructs the RBF proxy model using samples in the database during the evolution process; S4, the parent population is mutated and crossovered according to five mutation strategies to obtain five offspring populations; S5, using the proxy model to evaluate the objective function value of the offspring individual and perform a selection operation, passing the parameters of the best individual in each offspring population to the electromagnetic ultrasonic transducer model and performing simulation evaluation, and updating the database and the global best individual in the subpopulation; S6, select the most promising one from the five offspring populations as the next generation population; S7, determining whether the maximum number of true evaluation times has been reached; If so, stop the evolution and output the optimal parameter design solution; otherwise, go to S3 to continue the search process.

2. The rapid design method of electromagnetic ultrasonic transducer based on S-SADE algorithm according to claim 1 is characterized in that: The specific steps of S1 include: S11, establishing a finite element model for electromagnetic ultrasonic transducer simulation in COMSOL simulation software with multiple physical field modules, selecting materials and setting parameters, selecting and setting physical fields for the constructed model, adding excitation sources to the model and performing meshing; S12, the constructed finite element model uses the AC / DC module and the magnetic field module, and the structural mechanics module for the generation and transmission of SV waves; the finite element model consists of a CML coil, a cylindrical magnet, an aluminum plate, surrounding air, and an infinite domain; S13, the optimization goal is to maximize the single-point signal amplitude of the designed electromagnetic ultrasonic transducer model, and the objective function can be defined as: Maxf(x)=A w，i(x) Among them, A w,i(x) is the signal amplitude at the ith observation point, and each observation point x = [R, H, W, L, I] contains multiple optimized parameters, where R is the magnet radius, H is the magnet height, W is the CML coil width, L is the lifting distance, and I is the alternating current; S14, build a MATLAB-COMSOL joint simulation platform, call the COMSOL model in Matlab, modify the optimization variable values ​​and simulate the calculation and output the optimization target, so as to realize the automatic operation of the electromagnetic ultrasonic transducer parameter optimization algorithm and the real-time feedback of the simulation results.

3. The rapid design method of electromagnetic ultrasonic transducer based on S-SADE algorithm according to claim 1 is characterized in that: The specific steps of S2 include: S21, initialization population size NP, maximum number of true evaluations F max , the maximum number of iterations G m , learn algebraic LP; S22, randomly generate 100 sets of data as initial population individuals within the parameter variation range of each optimization variable, that is, each individual corresponds to a set of optimization parameters; S23, transmit the data of each individual in the initial population to COMSOL software through the MATLAB-COMSOL joint simulation platform, set the parameters of the electromagnetic ultrasonic transducer and run the simulation to calculate the single-point signal amplitude, output the results to Matlab, obtain 100 real samples and store them in the database; S24, for the maximization problem, sort the initial samples from large to small according to the objective function value, select the first NP individuals with larger objective function values ​​as the initial population, and store them in the database.

4. The rapid design method of electromagnetic ultrasonic transducer based on S-SADE algorithm according to claim 1 is characterized in that: The specific steps of S3 include: S31, if the number of samples in the database is greater than 2NP, sort them from large to small according to the objective function value, retain the first 2NP samples, and delete the other samples; S32, randomly selecting NP samples from the database as training data for training the RBF proxy model.

5. The rapid design method of electromagnetic ultrasonic transducer based on S-SADE algorithm according to claim 1 is characterized in that: The specific steps of S4 include: S41, adaptively adjust the mutation operator F, the mutation operator F controls the amplification effect of the deviation variable, in order to balance the global search ability and local development ability of the algorithm, set the mutation operator F in each generation to change dynamically according to the following formula: Among them, G m represents the maximum number of iterations, gen represents the current number of iterations, and F0 is a constant set to 0.4; S42, mutation operation. The differential evolution algorithm uses mutation operation to generate mutation vectors for each individual in the current population. For each target vector x i,G , generate mutation vector v according to the following five mutation methods i,G ; 1)DE / rand / 1: 2)DE / best / 1: 3)DE / rand-to-best / 1: 4)DE / best / 2: 5)DE / rand / 2: In the formula, x best,G is the best individual in the current population; the randomly selected numbers r1, r2, r3, r4, r5 are different from the target vector number i; S43, adaptively adjust the crossover operator CR. Before the LP generation, the crossover operator CR of the k-th mutation strategy is composed of the normal distribution N(CRm k , std) generates a set of CR values ​​and assigns them to each target vector; Among them, CRm k and std are the mean and variance respectively, and CRm is initialized k The value of is 0.5, and the value of std is 0.1; At the same time, a CRMemory k The archive is used to store the CR values ​​of the trial vectors generated by the k-th strategy in the previous LP generation that can successfully enter the next generation. After the LP generation, CRMemory k The archive stores the CR values ​​of the LP generation before the current one; in the previous LP generation, the CR value of the kth strategy is generated by N(0.5, 0.1); in each generation after the LP generation, CRm k Set to CRMemory k The average value of the data is stored in the CR value of the k-th strategy is N(CRm k , 0.1) generated; S44, crossover operation, in order to increase the diversity of the interference parameter vector, a binomial crossover operation is introduced after the mutation operation is completed to generate the test vector u ji,G : (i=1,2,…,NP; j=1,2,…,D) Where randb(j) represents the jth estimated value of a random number generator between [0,1]; D represents the dimension of the problem; rnbr(i)∈1,2,…,D represents a randomly selected sequence.

6. The rapid design method of electromagnetic ultrasonic transducer based on S-SADE algorithm according to claim 1 is characterized in that: The specific steps of S5 include: S51, using the contemporary trained RBF surrogate model to predict the objective function value of the offspring individuals; S52, selection operation, to determine the test vector u i,G Will it enter the next generation population? According to the greedy rule, the test vector u i,G With the target vector x i,G For comparison, if the test vector u i,G The objective function value of is larger, then the test vector u i,G It is retained in the next generation population and its CR value is stored in CRMemory k middle; S53, selecting the individuals with the largest objective function value in each offspring population as filling solutions, transmitting their values ​​to COMSOL simulation software through the MATLAB-COMSOL joint simulation platform, setting parameters for the electromagnetic ultrasonic transducer, and running the simulation to calculate the single-point signal amplitude, outputting the results to Matlab and updating its objective function value; S54, determining whether the individual is in the database; if not, adding the individual and its objective function value to the database; S55: Compare the objective function value of the individual with the global optimal individual of the current subpopulation. If the objective function value of the individual is larger, update the individual to the global optimal individual.

7. The rapid design method of electromagnetic ultrasonic transducer based on S-SADE algorithm according to claim 1 is characterized in that: The specific steps of S6 include: S61, to select the most promising population from the five sub-populations obtained in S5, calculate the average fitness value of all individuals in each sub-population, and the population with the largest average fitness value will be used as the initial population of the next generation. The calculation formula is as follows: S62, using the selected sub-population as the initial population of the next generation, and updating the global optimal individual in the sub-population to the global optimal individual.

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