Phased-array antenna beam broadening optimization method and system

Abstract

The invention discloses a phased-array antenna beam broadening optimization method and system, and belongs to the technical field of array antenna design. According to the method, the ambiguity and randomness of addressing are quantized by adopting a self-iteration model, the speed and direction of particle addressing are dynamically adjusted by combining parameters such as expectation and entropy, and the optimization efficiency is greatly improved; grading weights are adopted during cross fusion, so that screening of particles of different levels is facilitated, different weights are assigned to different particles, invalid movement of the particles is effectively avoided, and the probability of finding a better solution is improved while the particles tend to a globally optimal solution; when the method is applied to a radar system, the radiation performance of the system can be effectively improved, and the reconnaissance range of the system is widened.

Description

Technical Field

[0001] The present invention relates to the technical field of array antenna design, and particularly to a method and system for optimizing the beam broadening of a phased array antenna. Background Art

[0002] The particle swarm optimization algorithm is widely used because it can effectively solve complex problems. It was born from the bird migration model, and the law of bird migration was abstracted to form the basic particle swarm optimization algorithm. The basic particle swarm optimization algorithm optimizes and solves variables in a continuous domain through iteration, but it has deficiencies such as low accuracy, easy convergence, and difficulty in applying to discrete problems.

[0003] The traditional beam broadening optimization method is to perform square or cubic weighting on the phase at the antenna aperture, but such methods are difficult to broaden the beam efficiently by a large multiple and it is difficult to balance the requirements of efficiency and sidelobes. Therefore, an optimization algorithm needs to be used for its non-linear optimization. When the antenna scale is larger, the problem is more complex, and the obtained optimization result is more prominent than the traditional method.

[0004] How to improve the optimization efficiency of the beam broadening of an array antenna while taking into account radiation performance such as sidelobes is an urgent problem to be solved. For this reason, a method for optimizing the beam broadening of a phased array antenna is proposed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: how to improve the optimization efficiency of the beam broadening of an array antenna while taking into account radiation performance such as sidelobes, and a method for optimizing the beam broadening of a phased array antenna is provided.

[0006] The present invention solves the above technical problem through the following technical solutions. The present invention includes the following steps:

[0007] S1: Set array parameters and objectives

[0008] Obtain the phased array antenna scale according to the decomposition of system indicators, calculate the antenna element spacing according to the scanning or beam coverage range; set the beam width and sidelobe optimization objectives according to the overall indicator requirements, and construct a fitness function;

[0009] S2: Improvement of the discretization of the optimization model

[0010] Decompose the amplitude-phase optimization of a two-dimensional phased array antenna into the amplitude-phase optimization of two one-dimensional phased array antennas. When broadening the beam, the phase needs to be optimized. Before optimization, convert the phases of N antenna elements into binary codes and merge them into an array, which is used as the information carried by a particle in the particle swarm optimization algorithm, where N is the one-dimensional scale of the phased array antenna;

[0011] S3: Self-iterative weight design

[0012] An optimization algorithm is established based on the particle swarm algorithm to calculate the ratio η between the result obtained in the t-th generation and the optimal solution. Different weight coefficients are set according to different values of the ratio η and in combination with the self-iteration model, that is, the inertial weight ω is introduced. i Meanwhile, a self-iterative particle swarm model is obtained.

[0013] S4: Cross-fusion of population information

[0014] The information carried by a certain particle in the t-th generation in the population is cross-fused with the information in the local population and the global optimal solution respectively according to the inertial weight ω. i By doing so, the particle carries partial information of the local population and the global optimal solution, that is, the result after iterative optimization in the t-th generation is obtained, which is used as the population for optimization iteration in the (t + 1)-th generation.

[0015] S5: Repeated iteration for convergence

[0016] After the cross-fusion is completed, a mutation operation is performed on each particle; after each iteration, the information cross-fusion and mutation of all particles in each population are completed, and the iteration is repeated until the maximum number of iterations is reached or the fitness function converges.

[0017] Furthermore, in the step S1, to take into account the sidelobe constraint, the fitness function is set as:

[0018]

[0019] where Sll is the current maximum sidelobe level, Sll0 is the target maximum sidelobe level, Bw is the current beam width, Bw0 is the target beam width; adjusting the weighting coefficients α1 and α2 determines the proportion of the sidelobe and the beam width in the optimization; the optimization goal is to calculate the minimum value of fitness.

[0020] Furthermore, in the step S1, when the antenna element spacing of the phased array antenna is d and the amplitude of the n-th antenna element is a n , then the field strength pattern after phase weighting is:

[0021]

[0022] where is the phase of the n-th antenna element, θ is the observation direction angle, θ0 is the maximum pointing angle of the phased array antenna, and λ is the wavelength corresponding to the operating frequency.

[0023] Furthermore, in the step S2, to optimize the phase, that is, to perform phase weighting, the way of phase weighting is to add digital phase shifters. When the number of bits of the digital phase shifter is K, the phase of the antenna element after phase weighting is:

[0024]

[0025] Among them, k1 to k K are the phase shift weights for each bit of the phase shifter;

[0026] Each particle corresponds to the phases of N antenna elements. M particles form one population. Multiple populations are randomly generated during the optimization initialization, and the information of all particles in all populations is updated during each iteration.

[0027] Furthermore, in the step S3, the self-iteration model quantifies fuzziness and randomness through the expected value Ex, entropy En, and hyperentropy He. Assume that a single random realization of any x in the effective domain U satisfies x ~ N(Ex - En′ 2 ), and the random value of x is defined as:

[0028]

[0029] Among them, En′ is a normal distribution random number with En as the expected value and He as the standard deviation, that is:

[0030]

[0031] Furthermore, in the step S3, in the self-iteration particle swarm model, the temporary position of the particle is the current optimal solution position. After the t-th generation of iteration, the particles with a relatively small difference from the temporary position slow down, and the particles with a relatively large difference increase their speed.

[0032] Furthermore, in the step S3, for any particle i, the value of the inertia weight ω i is as follows:

[0033] When 0 < η i ≤ A, it is considered that the position of this particle is relatively different from the current optimal solution position. At this time, ω i = 0.8;

[0034] When A < η i ≤ B, it is considered that the position of this particle is moderately different from the current optimal solution position. At this time

[0035] When B < η i < 1, it is considered that the position of this particle is relatively different from the current optimal solution position. At this time, ω i = 0.2;

[0036] Among them, A and B are screening coefficients used to adjust the proportions of the three types of particles in the total number of particles.

[0037] Furthermore, in the step S4, during the iteration, the information carried by each particle is updated, and the optimal solutions of the population and the global are updated synchronously.

[0038] Further, in the step S5, the mutation operation refers to flipping the bits of the particle's information according to the set proportionality coefficient μ. The current bit flip means changing from 0 to 1 or from 1 to 0; the number of flipped bits is the total number of bits of the information multiplied by the proportionality coefficient μ, and the bit numbers to be flipped are randomly generated; the set proportionality coefficient μ is taken as 0.02.

[0039] The present invention also provides a phased array antenna beam broadening optimization system, which uses the above method to improve the optimization efficiency of the array antenna beam broadening, including:

[0040] An array parameter and target setting module, which is used to decompose the phased array antenna scale according to the system index, calculate the antenna element spacing according to the scanning or beam coverage range; set the beam width and sidelobe optimization target according to the overall index requirements, and construct a fitness function;

[0041] A discretization improvement module, which is used to decompose the amplitude-phase optimization of the two-dimensional phased array antenna into the amplitude-phase optimization of two one-dimensional phased array antennas. When broadening the beam, the phase needs to be optimized. Before optimization, the phases of N antenna elements are converted into binary codes and merged into an array, which is used as the information carried by a particle in the particle swarm algorithm, where N is the one-dimensional scale of the phased array antenna;

[0042] A self-iterative weight design module, which is used to establish an optimization algorithm based on the particle swarm algorithm, calculate the ratio η of the result obtained in the t-th generation to the optimal solution, and set different weight coefficients according to different values of the ratio η and in combination with the self-iterative model, that is, introduce the inertia weight ω i , and at the same time obtain a self-iterative particle swarm model;

[0043] A cross-fusion module, which is used to cross-fuse the information carried by a certain particle in the t-th generation in the population with the information of the population and the global optimal solution according to the inertia weight ω i to make the particle carry partial information of the population and the global optimal solution, that is, obtain the result after the t-th generation of iterative optimization, which is used as the population for the (t + 1)-th generation of optimization iteration;

[0044] An iterative convergence module, which is used to perform a mutation operation on each particle after completing the cross-fusion; after each iteration, the information cross-fusion and mutation of all particles in each population are completed, and the iteration is repeated until the maximum iteration number or the fitness function converges.

[0045] The present invention has the following advantages compared with the prior art:

[0046] 1. The introduction of the self-iterative model makes the particle optimization more intelligent. In each iteration, the particle needs to assign weight coefficients according to the group optimal solution and the global optimal solution respectively; after carrying the information of the group and the system optimal solution at the same time, the proportion of particles that find better positions in the group in the next iteration is larger, and the possibility of finding the optimal solution after multiple iterations is higher.

[0047] 2. Introducing a mutation mechanism can effectively avoid the local convergence of the optimization algorithm, jump out of the range of the local optimal solution, and increase the probability of finding a better solution.

[0048] 3. Using a self-iterative model to quantify the ambiguity and randomness of addressing, and dynamically adjusting the speed and direction of particle addressing by combining parameters such as expectation and entropy, which greatly improves the optimization efficiency; when using hierarchical weights during cross-fusion, it is beneficial to screen particles at different levels and assign different weights to different particles, effectively avoiding the ineffective movement of particles, and increasing the probability of finding a better solution while the particles tend to the global optimal solution; when this method is applied to a radar system, it can effectively improve the radiation performance of the system and increase the reconnaissance range of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a schematic flow chart of the phased array antenna beam broadening optimization method in Embodiment 1 of the present invention;

[0050] Figure 2 is the radiation pattern after beam broadening in Embodiment 2 of the present invention;

[0051] Figure 3 is the radiation pattern after beam broadening in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following provides a detailed description of the embodiments of the present invention. These embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0053] Embodiment 1

[0054] This embodiment provides a phased array antenna beam broadening optimization method, which ultimately realizes how to improve the optimization efficiency of the beam broadening of the array antenna while taking into account radiation performance such as sidelobes. The present invention includes the following steps:

[0055] S1: Set array parameters and objectives

[0056] Obtain the phased array antenna scale by decomposing the system index, and calculate the antenna element spacing according to the scanning or beam coverage range; set the beam width and sidelobe optimization objectives according to the overall index requirements, and construct a fitness function;

[0057] S2: Improvement of the discretization of the optimization model

[0058] The amplitude-phase optimization of a two-dimensional phased array antenna can be decomposed into the amplitude-phase optimization of two one-dimensional phased array antennas. Assuming that the one-dimensional scale of the phased array antenna is N, phase optimization is required during beam broadening. Before optimization, binary coding conversion is performed on the phases of N antenna elements, and they are combined into an array as the information carried by one particle in the particle swarm algorithm.

[0059] S3: Self-iterative weight design

[0060] The optimization algorithm is based on the particle swarm algorithm. When the optimization weight is a fixed value, particles are not screened during the iterative process, and all particles approach the swarm and global optimal solutions at the same speed. At this time, the fitness value distribution of the particles in the population changes little after one iteration, and there is a high possibility of falling into local convergence. The obtained sub-optimal solution may not necessarily be the optimal solution. To better avoid premature convergence, an inertia weight ω is introduced into the optimization algorithm. i ;

[0061] S4: Population information crossover and fusion

[0062] The population for the optimization iteration in the (t + 1)-th generation is based on the results obtained in the t-th generation: The information carried by a certain particle in the t-th generation in the population is cross-fused with the information of this population and the global optimal solution according to the inertia weight ω. i After that, this particle will carry partial information of this population and the global optimal solution.

[0063] S5: Repeated iteration and convergence

[0064] After completing the crossover and fusion, a mutation operation is performed on each particle. After each iteration, the information crossover and fusion and mutation of all particles in each population are completed, and the iteration is repeated until the maximum number of iterations is reached or the fitness function converges.

[0065] In this embodiment, in the step S1, it is assumed that the antenna element spacing of the phased array antenna is d. If the amplitude of the n-th antenna element is a n , then the field strength pattern after phase weighting is:

[0066]

[0067] Among them, is the phase of the n-th antenna element, θ is the observation direction angle, θ0 is the maximum pointing angle of the phased array antenna, λ is the wavelength corresponding to the operating frequency. To take into account the sidelobe constraint, the fitness function is set as:

[0068]

[0069] Where Sll is the current maximum sidelobe level, Sll0 is the target maximum sidelobe level, Bw is the current beam width, and Bw0 is the target beam width; the weighting coefficients α1 and α2 are adjusted to determine the proportion of sidelobe and beam width in the optimization; the optimization goal is to calculate the minimum value of fitness.

[0070] In this embodiment, in step S2, the method of implementing phase weighting in engineering is to add a digital phase shifter. If the number of bits of the digital phase shifter is K, the phase that can be achieved by the antenna unit is:

[0071]

[0072] Among them, k1 to k K The weight of each phase shift of the phase shifter is 1 by default;

[0073] Each particle corresponds to the phase of N antenna units, and M particles form a population. Multiple populations are randomly generated during optimization initialization, and information of all particles in all populations is updated in each iteration.

[0074] In this embodiment, in step S3, the ratio η of the result obtained in the tth generation to the optimal solution is calculated. Assuming that the value of the i-th particle obtained in the tth generation is p and the current global optimal solution is q, the ratio η of the i-th particle is i =p / q. Different weight coefficients are set according to different values of η. The weight of the second case also needs to be obtained by combining the self-iterative model. The model quantifies fuzziness and randomness through three parameters: expectation Ex, entropy En, and super entropy He. Assume that a random realization of any x in the valid domain U satisfies x~N(Ex-En′ 2 ), define the random value of x as:

[0075]

[0076] Among them, En′ is a normally distributed random number with En as the expected value and He as the standard deviation, that is:

[0077]

[0078] In this embodiment, in step S3, in the self-iterative particle swarm model, the temporary position of the particle is the current optimal solution position. After the tth iteration, the particles with a small difference from the temporary position slow down, and the particles with a large difference increase their speed, which ensures that the particles as a whole are closer to the known optimal solution, providing a better starting point for the next addressing. Taking any particle i as an example, the weight of the cross-fusion is as follows:

[0079] a) When 0<η i ≤A, it is considered that the particle position is significantly different from the current optimal solution position, and ω is taken at this time. i= 0.8;

[0080] b) When A < η i ≤ B, it is considered that the position of the particle is appropriate. At this time, take:

[0081]

[0082] c) When B < η i < 1, it is considered that the position of the particle is better. The current position is already close to the known optimal solution position. Only a smaller weight coefficient needs to be used. Take ω i = 0.2.

[0083] Among them, A and B are screening coefficients, which are used to adjust the proportion of the three types of particles in the total number of particles respectively.

[0084] In this embodiment, in the step S5, the mutation operation refers to flipping the bits of the particle information according to a specific proportional coefficient μ. The current bit flip means changing from 0 to 1 or from 1 to 0; the number of flipped bits is the total number of bits of the information multiplied by the proportional coefficient μ, and the bit numbers to be flipped are randomly generated; the proportional coefficient μ is usually taken as 0.02.

[0085] In this embodiment, in the step S4, when iterating, the fitness values of the particles in the population are updated. To improve the optimization efficiency, the population and the global optimal solution are updated synchronously.

[0086] Embodiment Two

[0087] This embodiment optimizes a linear array with N = 60, a center frequency of 3 GHz, and an element spacing of half a wavelength. A 5-bit phase shifter is used. To balance the sidelobes, the sidelobe level is screened during initialization. The weighting coefficients of the fitness function are 0.35 and 0.65 respectively. During optimization, a mutation operation is performed, and the information of the particles is randomly changed according to the mutation coefficient. The mutation operation is an important source for obtaining a better solution in the next iteration.

[0088] This embodiment sets the sidelobe below -5 dB. The iteration results are as Figure 2 shown Figure 2 for the direction pattern after beam broadening. The antenna sidelobe level is -5.9 dB, and the beam width is 7.2 deg. When no phase weighting is performed, the antenna beam width is 2.82 deg. It can be obtained that the antenna beam width is broadened to 2.5 times after optimization. During the algorithm iteration, the weighting coefficients of the fitness function can be adjusted according to requirements, and different optimization results will be obtained. When adjusting the values of A, B, and the mutation coefficient, the optimization ability of the algorithm and the ability to escape from local better solutions also change.

[0089] Embodiment Three

[0090] In this embodiment, a linear array with N = 32 is optimized. The center frequency is 8 GHz, and the element spacing is half a wavelength. A 5-bit phase shifter is used. To balance the side lobes, the side lobe level is screened during initialization. The weighting coefficients of the fitness function are 0.3 and 0.7 respectively. During optimization, a mutation operation is performed, and the information of the particles is randomly changed according to the mutation coefficient. The mutation operation is an important source for obtaining a better solution in the next iteration.

[0091] In this embodiment, the side lobe is set to be lower than -10 dB. The iteration results are as Figure 3 shown Figure 3 in the pattern after beam broadening. The antenna side lobe level is -11.4 dB, and the beam width is 9.6 deg. When no phase weighting is performed, the antenna beam width is 3.18 deg. It can be seen that the antenna beam width is broadened to 3 times after optimization. During the algorithm iteration, the weighting coefficients of the fitness function can be adjusted according to requirements, and different optimization results will be obtained. When adjusting the values of A, B, and the mutation coefficient, the optimization ability of the algorithm and the ability to escape from local optimal solutions also change.

[0092] In summary, for the phased array antenna beam broadening optimization method of the above embodiment, the self-iteration model is used to quantify the ambiguity and randomness of addressing, and parameters such as expectation and entropy are combined to dynamically adjust the speed and direction of particle addressing, greatly improving the optimization efficiency; when cross-fusing, graded weights are used, which is beneficial to screening particles at different levels and assigning different weights to different particles, effectively avoiding the ineffective movement of particles, and increasing the probability of finding a better solution while the particles tend to the global optimal solution; when this method is applied to a radar system, it can effectively improve the radiation performance of the system and increase the reconnaissance range of the system.

[0093] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing beam broadening of a phased array antenna, characterized in that, It includes the following steps: S1: Set array parameters and targets Decompose according to system indicators to obtain the phased array antenna scale, and calculate the antenna element spacing based on the scanning or beam coverage range; Set the beam width and sidelobe optimization targets according to the overall indicator requirements, and construct a fitness function; S2: Improvement of optimization model discretization Decompose the amplitude-phase optimization of the two-dimensional phased array antenna into the amplitude-phase optimization of two one-dimensional phased array antennas. When beam broadening, the phase needs to be optimized. Before optimization, perform binary coding conversion on the phases of N antenna elements and merge them into an array, which serves as the information carried by a particle in the particle swarm algorithm, where N is the one-dimensional scale of the phased array antenna; S3: Self-iterative weight design An optimization algorithm is established based on the particle swarm algorithm to calculate the ratio η of the result obtained in the t-th generation to the optimal solution. Different weight coefficients are set according to different values of the ratio η and in combination with the self-iteration model, that is, the inertial weight ω is introduced. i At the same time, a self-iterative particle swarm model is obtained. S4: Cross-fusion of population information The information carried by a certain particle in the population at the t-th generation is cross-fused with the information of the population itself and the global optimal solution according to the inertial weight ω i to enable the particle to carry partial information of the population itself and the global optimal solution, that is, to obtain the result after iterative optimization at the t-th generation, which is used as the population for optimization iteration at the (t + 1)-th generation; S5: Repeated iteration for convergence After completing the cross-fusion, perform mutation operations on each particle; After each iteration, complete the information cross-fusion and mutation of all particles in each population, and repeat the iteration until the maximum iteration number is reached or the fitness function converges.

2. The phased array antenna beam broadening optimization method according to claim 1, wherein In the step S1, in order to take into account the sidelobe constraint, the fitness function is set as: Where Sll is the current maximum sidelobe level, Sll0 is the target maximum sidelobe level, Bw is the current beam width, and Bw0 is the target beam width; Adjust the weighting coefficients α1 and α2 to determine the proportion of sidelobes and beam width in the optimization; The optimization goal is to calculate the minimum value of fitness.

3. The phased array antenna beam broadening optimization method according to claim 2, characterized in that In the step S1, when the antenna element spacing of the phased array antenna is d and the amplitude of the nth antenna element is a n the field strength pattern after phase weighting is as follows: wherein, is the phase of the nth antenna element, θ is the observation direction angle, θ0 is the maximum pointing angle of the phased array antenna, and λ is the wavelength corresponding to the operating frequency.

4. A phased array antenna beam broadening optimization method according to claim 3, characterized in that, In the step S2, optimizing the phase means performing phase weighting. The way of phase weighting is to add digital phase shifters. When the number of bits of the digital phase shifter is K, the phase of the antenna element after phase weighting is: Among them, k1 to k K is the weight value of phase shift per bit of the phase shifter; Each particle corresponds to the phases of N antenna elements, and M particles form one population. When initializing the optimization, multiple populations are randomly generated, and the information of all particles in all populations is updated during each iteration.

5. A phased array antenna beam broadening optimization method according to claim 1, characterized in that, In the step S3, in the step S3, the self-iterative model quantifies fuzziness and randomness through the expected value Ex, entropy En, and hyperentropy He. It is assumed that a single random realization of any x in the effective domain U satisfies x ∼ N(Ex - En′ 2 ), and the random value of x is defined as: Where En′ is a normally distributed random number with En as the expected value and He as the standard deviation, that is:

6. A method for optimizing beam broadening of a phased array antenna according to claim 5, characterized in that In the step S3, in the self-iterative particle swarm model, the temporary position of the particle is the current optimal solution position. After the t-th generation of iteration, the particles with a relatively small difference from the temporary position slow down, and the particles with a relatively large difference increase their speed.

7. A phased array antenna beam broadening optimization method according to claim 6, characterized in that, In the step S3, for any particle i, the inertial weight ω i is taken as follows: When 0 < η i ≤ A, it is considered that the position of the particle is quite different from the position of the current optimal solution. At this time, ω i = 0.8; When A < η i ≤ B, it is considered that the position of the particle is moderately different from the current optimal solution position. At this time When B < η i < 1, it is considered that the position of the particle is less different from the current optimal solution position. At this time, ω i = 0.2; Where A and B are screening coefficients, which are used to adjust the proportion of the three types of particles in the total number of particles.

8. A method for optimizing beam broadening of a phased array antenna according to claim 1, characterized in that In the step S4, when iterating, update the information carried by each particle, and synchronously update the optimal solutions of the population and the global.

9. A method for optimizing the beam broadening of a phased array antenna according to claim 1, characterized in that, In the step S5, the mutation operation refers to flipping the number of bits of the particle's information according to the set proportional coefficient μ. The current bit flip means changing from 0 to 1 or from 1 to 0; The number of flipped bits is the total number of bits of the information multiplied by the proportional coefficient μ, and the bit numbers to be flipped are randomly generated; The set proportional coefficient μ is taken as 0.

02.

10. A phased array antenna beam broadening optimization system, characterized in that, Using the optimization method according to any one of claims 1 to 9 to improve the optimization efficiency of beam broadening of the array antenna, including: An array parameter and target setting module, which is used to decompose according to system indicators to obtain the phased array antenna scale, and calculate the antenna element spacing based on the scanning or beam coverage range; Set the beam width and sidelobe optimization targets according to the overall indicator requirements, and construct a fitness function; The discretization improvement module is used to decompose the amplitude-phase optimization of a two-dimensional phased array antenna into the amplitude-phase optimization of two one-dimensional phased array antennas. When beam broadening, the phase needs to be optimized. Before optimization, the phases of N antenna elements are converted by binary coding and merged into an array, which serves as the information carried by a particle in the particle swarm algorithm, where N is the one-dimensional scale of the phased array antenna; Self-iterative weight design module, which is used to establish an optimization algorithm based on the particle swarm algorithm, calculate the ratio η of the result obtained in the t-th generation to the optimal solution, set different weight coefficients according to different values of the ratio η and in combination with the self-iterative model, that is, introduce the inertia weight ω i , and at the same time obtain a self-iterative particle swarm model; The cross-fusion module is used to cross-fuse the information carried by a certain particle in the population at the t-th generation with the information of the population itself and the global optimal solution according to the inertia weight ω i to perform cross-fusion, so that the particle carries partial information of the population itself and the global optimal solution, that is, to obtain the result after iterative optimization at the t-th generation, which is used as the population for iterative optimization at the (t + 1)-th generation; The iterative convergence module is used to perform a mutation operation on each particle after completing the cross-fusion; after each iteration, the information cross-fusion and mutation of all particles in each group are completed, and the iteration is repeated until the maximum number of iterations is reached or the fitness function converges.

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