A multi-strategy combined improved sparse planar array optimization method and system

Abstract

The present invention discloses a multi-strategy combined improved sparse planar array optimization method and system. The method includes: calculating the peak sidelobe level of each initial element distribution to obtain the current lowest peak sidelobe level and the optimal element distribution corresponding to the lowest peak sidelobe level, and updating the element positions according to the ascending stage, descending stage, and landing stage in the improved dandelion optimization algorithm; starting the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculating the target lowest peak sidelobe level of the updated element distribution, and if the value of the target lowest peak sidelobe level is greater than the value of the lowest peak sidelobe level, replacing it with the updated element distribution; updating the global optimal element distribution position and the corresponding lowest peak sidelobe level. It improves the efficiency of the algorithm in global search and local development, avoids the algorithm falling into local optimum prematurely, and thus improves the solution accuracy and convergence speed of the algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of array antenna processing, and particularly relates to a sparse planar array optimization method and system with combined improvement of multiple strategies. Background Art

[0002] Laser antennas have the characteristics of long transmission distance and fast transmission speed because their laser beams can maintain a high energy density and a small divergence angle; while sparse array antennas can obtain desired peak sidelobe level (PSLL), main lobe width, directivity coefficient and other indicators by controlling the number and position of array elements in a uniform array, so that the antenna has performances such as high gain, narrow beam and low sidelobe. However, the design of sparse laser array antennas is a complex non-linear optimization problem. How to effectively and quickly determine the distribution of array element parameters through an optimization method to obtain the required radiation pattern is crucial for array antenna design.

[0003] By using computer technology to model and identify the parameters of the array antenna, and then performing a global search through an optimization algorithm, the lowest peak sidelobe level can finally be obtained, so as to determine the optimal layout scheme of the array antenna. The existing algorithms for array antenna optimization mainly include genetic algorithm, particle swarm algorithm and invasive weed optimization algorithm. These swarm intelligence algorithms have been widely applied in the field of industrial optimization production. However, the design of sparse laser array antennas is a complex non-linear optimization problem, and these traditional algorithms cannot quickly and accurately determine the array element parameters and distribution to obtain the required radiation pattern. Therefore, finding an efficient optimization method that can balance global and local searches has become an important issue in array antenna optimization. Summary of the Invention

[0004] The present invention provides a sparse planar array optimization method and system with combined improvement of multiple strategies to solve the technical problem that the array element parameters and distribution cannot be quickly and accurately determined.

[0005] In a first aspect, the present invention provides a sparse planar array optimization method with combined improvement of multiple strategies, including:

[0006] Initializing the positions of array elements according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate an initial array element distribution with uniform distribution;

[0007] Calculating the peak sidelobe level of each initial array element distribution, obtaining the current lowest peak sidelobe level and the best array element distribution corresponding to the lowest peak sidelobe level, and updating the positions of array elements according to the ascending stage, descending stage and landing stage in the improved dandelion optimization algorithm;

[0008] Start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculate the target minimum peak sidelobe level of the updated element distribution. If the value of the target minimum peak sidelobe level is greater than the value of the minimum peak sidelobe level, replace it with the updated element distribution;

[0009] Update the global optimal element distribution position and the corresponding minimum peak sidelobe level;

[0010] Judge whether the termination condition is satisfied. If not, update the iteration number and perform iteration. Otherwise, output the global optimal element distribution and the minimum peak sidelobe level of each generation.

[0011] In a second aspect, the present invention provides a multi-strategy joint improved sparse planar array optimization system, including:

[0012] An initialization module configured to initialize the element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial element distribution;

[0013] A first update module configured to calculate the peak sidelobe level of each initial element distribution, obtain the current minimum peak sidelobe level and the best element distribution corresponding to the minimum peak sidelobe level, and update the element positions according to the ascending stage, descending stage, and landing stage in the improved dandelion optimization algorithm;

[0014] A calculation module configured to start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculate the target minimum peak sidelobe level of the updated element distribution. If the value of the target minimum peak sidelobe level is greater than the value of the minimum peak sidelobe level, replace it with the updated element distribution;

[0015] A second update module configured to update the global optimal element distribution position and the corresponding minimum peak sidelobe level;

[0016] An output module configured to judge whether the termination condition is satisfied. If not, update the iteration number and perform iteration. Otherwise, output the global optimal element distribution and the minimum peak sidelobe level of each generation.

[0017] In a third aspect, there is provided an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the multi-strategy joint improved sparse planar array optimization method according to any embodiment of the present invention.

[0018] Fourthly, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the processor is caused to execute the steps of the multi-strategy combined improvement sparse planar array optimization method according to any embodiment of the present invention.

[0019] For the multi-strategy combined improvement sparse planar array optimization method and system of the present application, the Logistic-sin-cos chaotic mirror reverse learning strategy is adopted to form a uniform and diverse dandelion initial population to improve the global search ability of the algorithm; the adaptive step size strategy increases the population mutation probability of the algorithm and the possibility of jumping out of the local optimum; the local mutation search strategy that combines Cauchy inverse cumulative mutation and spiral flight optimizes the algorithm population, improves the efficiency of the algorithm in global search and local development, avoids the algorithm from falling into the local optimum prematurely, thereby improving the solution accuracy and convergence speed of the algorithm, and thus can better optimize the sparse laser array antenna, ensure the minimization of the peak sidelobe level, and has a more superior global search ability and higher optimization accuracy compared with the existing traditional technologies, providing a new method for the optimization of the sparse laser array antenna. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a flowchart of a multi-strategy combined improvement sparse planar array optimization method provided by an embodiment of the present invention;

[0022] Figure 2 It is a structural block diagram of a multi-strategy combined improvement sparse planar array optimization system provided by an embodiment of the present invention;

[0023] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0025] Assume a sparsity rate of and an array size of A rectangular planar sparse laser array with a grid spacing of , is the radiation wavelength, and the number of array elements is . Assuming it satisfies equal amplitude and omnidirectionality, according to the principle of electromagnetic wave superposition, the pattern function of the array can be expressed as:

[0026] ,

[0027] In the formula, is the radiation pattern direction function, is the working state of the array element at the position of ( m , n ), is the amplitude excitation coefficient of the array element at the position of ( m , n ), is the half-wavelength, is the conjugate complex number, = , is the elevation angle, is the azimuth angle, is the main lobe azimuth angle, is the main lobe elevation angle;

[0028] With the goal of reducing the peak sidelobe level of the pattern, under the condition of satisfying equal amplitude and omnidirectionality, search for the maximum sidelobe level in the two planes of and . According to the definition of PSLL, the fitness function is expressed as:

[0029] ,

[0030] In the formula, represents the sidelobe interval of the azimuth pattern when , represents the sidelobe interval of the elevation pattern when . The sparse laser array antenna optimization model can be defined as: , represents minimizing the peak sidelobe level.

[0031] Please refer to Figure 1 , which shows a flowchart of a multi-strategy joint improvement sparse planar array optimization method of the present application.

[0032] As Figure 1 shown, the multi-strategy joint improvement sparse planar array optimization method specifically includes the following steps:

[0033] Step S101, initialize the element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial element distribution.

[0034] In this step, the expression of the Logistic-sin-cos chaotic mapping in the Logistic-sin-cos (Logistic chaotic mapping, sin function, cos function) chaotic mirror reverse learning strategy is:

[0035] ,

[0036] where, is the updated position information of the dandelion, is the current position information of the dandelion, is a random number, and ;

[0037] While generating the initial solution using the Logistic-sin-cos chaotic mapping, introduce the mirror reverse learning strategy, and the expression of the mirror reverse learning strategy is:

[0038] ,

[0039] ,

[0040] where, is the position information of the reverse dandelion updated using the mirror reverse learning, is the upper limit of the current problem, is the lower limit of the current problem, is the dimension of the population, , , r are all random numbers uniformly distributed between 0 and 1, represents the multiplication sign.

[0041] Step S102, calculate the peak sidelobe level of each initial element distribution, obtain the current lowest peak sidelobe level and the best element distribution corresponding to the lowest peak sidelobe level, and update the element positions according to the ascending stage, descending stage and landing stage in the improved dandelion optimization algorithm.

[0042] In this step, the dandelion optimization algorithm updates and optimizes the position by simulating the behavior of a mature dandelion flying with the wind, and divides it into three stages: ascending, descending and landing. The diverse position update strategy of the dandelion optimization algorithm can explore the solution space more comprehensively and shows excellent performance when solving continuous and discrete optimization problems.

[0043] During the ascending stage, dandelion seeds are affected by factors such as wind speed and air humidity. Therefore, it can be classified into sunny days and rainy days according to the weather.

[0044] On sunny days, the wind speed follows a lognormal distribution, which is conducive to dandelion seeds flying far away. Therefore, the dandelion optimization algorithm on sunny days emphasizes exploration. Affected by the wind speed, the vortices above the dandelion seeds are constantly adjusted, showing a spiral upward shape. During this process, the expression corresponding to the ascending stage of the seeds is:

[0045] ,

[0046] ,

[0047] ,

[0048] ,

[0049] ,

[0050] ,

[0051] ,

[0052] ,

[0053] In the formula, is the position information of the dandelion population at the -th iteration, is the position information of the dandelion population at the -th iteration, is the adjustment factor for adjusting the local search area, is a random number between 0 and 1, q is an adaptive parameter, lnY represents following the lognormal distribution, is the mean value, is the variance, is the maximum number of iterations, and represent the lift component coefficients of the dandelion seed in the horizontal and vertical directions, is the adaptive step size factor, is the upper limit of the current problem, is the lower limit of the current problem, Dim represents the dimension of the problem, is a random number between, is the position randomly selected in the search space at the t-th iteration;

[0054] In rainy days, due to factors such as air humidity and air resistance, dandelion seeds cannot rise fully with the wind. At this time, the dandelion optimization algorithm emphasizes local area development, and the expression is:

[0055] ,

[0056] ,

[0057] ,

[0058] In the formula, is the adjustment factor for adjusting the local search area, is a random number between 0 and 1, and q is the adaptive parameter.

[0059] The analytical formula describing the rising stage of dandelion seeds is:

[0060] ,

[0061] In the descending stage, the dandelion optimization algorithm emphasizes global optimization. Using the average position information after the rising stage can not only reflect the stability of the dandelion's descent but also promote the dandelion population to move to the most ideal location for reproduction. The corresponding mathematical model in the descending stage is as follows:

[0062] ,

[0063] ,

[0064] In the formula, is the average position information of the dandelion population at the t-th iteration, is a random number, following Brownian motion with a normal distribution, is the number of the dandelion population, is the position information of the current dandelion, is the -th iteration of the position information of the dandelion population, is the -th iteration of the position information of the dandelion population.

[0065] In the landing stage, the dandelion optimization algorithm focuses on development. To enable the seeds to reach the location where they are most likely to survive, the current elite individual information can be used for local development, making the algorithm converge precisely to the global optimal solution. The expression is:

[0066] ,

[0067] ,

[0068] In the formula, is the position information of the optimal dandelion, is the Levy flight function, which is used to enhance the local search ability. is a linearly increasing function with values in [0, 2], which is used to avoid overexploitation and enable precise convergence to the global optimum.

[0069] It should be noted that during the landing phase, according to the selection probability determine the update strategy of the dandelion target position, and the expression is:

[0070] ,

[0071] In the formula, is the adjustment factor for adjusting the local search area, is the upper limit of the current problem, is the lower limit of the current problem, is the position information of the current dandelion, is the position information of the updated dandelion, is the position information of the optimal dandelion, is the Levy flight function, is a linearly increasing function with values in [0, 2], is the adaptive step size factor;

[0072] Among them, according to the selection probability determine the update strategy of the dandelion target position, which can perturb the dandelion population during the landing phase. Compared with most existing methods, this strategy has more advantages in enhancing population diversity and jumping out of local optima. The selection probability can make the algorithm focus on global search in the early stage and turn to jumping out of local optima in the later stage, which is beneficial to ensuring accurate convergence after sufficient global search.

[0073] The expression for calculating the selection probability is:

[0074] ,

[0075] In the formula, is the current iteration number, is the maximum iteration number.

[0076] Step S103, start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculate the target minimum peak sidelobe level of the updated element distribution. If the value of the target minimum peak sidelobe level is larger than the value of the minimum peak sidelobe level, then replace it with the updated element distribution.

[0077] In this step, the long-tail characteristic of the Cauchy inverse cumulative distribution helps the algorithm jump out of local optima and enhance the global search ability. The spiral flight strategy further expands the search range and avoids premature convergence to local optima. Compared with existing improved strategies, the proposed method can better balance the effects of global search and local exploitation and improve the convergence speed. The expression of the Cauchy spiral inverse cumulative distribution mutation strategy is as follows:

[0078] ,

[0079] ,

[0080] ,

[0081] where is the position information of the dandelion population at the -th iteration, is the position information of the optimal dandelion, is the position information of the dandelion population at the -th iteration, is a uniformly distributed random number, is the probability density function of the Cauchy inverse distribution, is 0, is 1, is a random number between [0, 1], consists of an exponential function based on e and varies with the number of iterations, is the spiral flight step size, with a value of 1, is the current iteration number, is the maximum iteration number.

[0082] Step S104, update the global optimal element distribution position and the corresponding lowest peak sidelobe level.

[0083] Step S105, determine whether the termination condition is satisfied. If not, update the iteration number and perform iteration. Otherwise, output the global optimal element distribution and the lowest peak sidelobe level of each generation.

[0084] In summary, based on the existing dandelion optimization algorithm, the method of this application introduces a Logistic-sin-cos chaotic mirror reverse learning strategy and a local mutation search strategy to generate a more uniform and higher-quality initial solution set. By integrating an adaptive step size strategy, the population mutation probability is increased, and the possibility of jumping out of the local optimum is enhanced. The local mutation search strategy adopts a Cauchy spiral inverse cumulative distribution mutation search strategy, which improves the efficiency of the algorithm in global search and local development. The organic combination of these strategies significantly enhances the global exploration ability and local optimum escape ability of the algorithm and effectively accelerates the convergence process of the algorithm. Applying the improved dandelion optimization algorithm to the solution of the sparse laser array antenna pattern optimization model can quickly obtain the optimal layout scheme and ensure the minimization of the peak sidelobe level.

[0085] Please refer to Figure 2 , which shows a structural block diagram of a multi-strategy joint improved sparse planar array optimization system of this application.

[0086] As Figure 2 shown, the multi-strategy joint improved sparse planar array optimization system 200 includes an initialization module 210, a first update module 220, a calculation module 230, a second update module 240, and an output module 250.

[0087] Among them, the initialization module 210 is configured to initialize the element positions according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial element distribution; the first update module 220 is configured to calculate the peak sidelobe level of each initial element distribution, obtain the current lowest peak sidelobe level and the best element distribution corresponding to the lowest peak sidelobe level, and update the element positions according to the rising stage, falling stage, and landing stage in the improved dandelion optimization algorithm; the calculation module 230 is configured to start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculate the target lowest peak sidelobe level of the updated element distribution, and if the value of the target lowest peak sidelobe level is larger than the value of the lowest peak sidelobe level, replace it with the updated element distribution; the second update module 240 is configured to update the global optimal element distribution position and the corresponding lowest peak sidelobe level; the output module 250 is configured to determine whether the termination condition is satisfied. If not, update the iteration count and perform iteration. Otherwise, output the global optimal element distribution and the lowest peak sidelobe level of each generation.

[0088] It should be understood that Figure 2 the modules described in Figure 1 correspond to the respective steps in the method described in reference Figure 2 . Therefore, the operations, features, and corresponding technical effects described above for the method also apply to the

[0089] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the processor is caused to execute the multi-strategy joint improvement sparse planar array optimization method in any of the above method embodiments;

[0090] As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as:

[0091] Initialize the element positions according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial element distribution;

[0092] Calculate the peak sidelobe level of each initial element distribution, obtain the current lowest peak sidelobe level and the best element distribution corresponding to the lowest peak sidelobe level, and update the element positions according to the ascending stage, descending stage, and landing stage in the improved dandelion optimization algorithm;

[0093] Start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculate the target lowest peak sidelobe level of the updated element distribution. If the value of the target lowest peak sidelobe level is larger than the value of the lowest peak sidelobe level, replace it with the updated element distribution;

[0094] Update the global optimal element distribution position and the corresponding lowest peak sidelobe level;

[0095] Judge whether the termination condition is satisfied. If not, update the iteration number and perform iteration. Otherwise, output the global optimal element distribution and the lowest peak sidelobe level of each generation.

[0096] The computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area may store an operating system and application programs required for at least one function; the storage data area may store data created according to the use of the multi-strategy joint improvement sparse planar array optimization system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the computer-readable storage medium may optionally include a memory remotely set relative to the processor, and these remote memories may be connected to the multi-strategy joint improvement sparse planar array optimization system through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0097] Figure 3 is a schematic structural diagram of the electronic device provided by the embodiments of the present invention, as Figure 3As shown in the figure, the device includes a processor 310 and a memory 320. The electronic device may further include an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected through a bus or other means. Figure 3 Taking the connection through the bus as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running non-volatile software programs, instructions, and modules stored in the memory 320, that is, implementing the multi-strategy joint improvement sparse planar array optimization method in the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the multi-strategy joint improvement sparse planar array optimization system. The output device 340 may include a display device such as a display screen.

[0098] The above electronic device can execute the method provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiment of the present invention.

[0099] As an implementation manner, the above electronic device is applied to a multi-strategy joint improvement sparse planar array optimization system for a client, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0100] Initialize the element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial element distribution;

[0101] Calculate the peak sidelobe level of each initial element distribution, obtain the current lowest peak sidelobe level and the optimal element distribution corresponding to the lowest peak sidelobe level, and update the element positions according to the ascending stage, descending stage, and landing stage in the improved dandelion optimization algorithm;

[0102] Start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the element positions, calculate the target lowest peak sidelobe level of the updated element distribution, and if the value of the target lowest peak sidelobe level is greater than the value of the lowest peak sidelobe level, replace it with the updated element distribution;

[0103] Update the global optimal element distribution position and the corresponding lowest peak sidelobe level;

[0104] Judge whether the termination condition is satisfied. If not, update the iteration count and perform iteration. Otherwise, output the global optimal element distribution and the lowest peak sidelobe level of each generation.

[0105] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A multi-strategy joint improved sparse array optimization method, characterized in that: include: The array element position is initialized according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution, wherein the expression of the Logistic-sin-cos chaotic map in the Logistic-sin-cos chaotic mirror reverse learning strategy is: ， In the formula, The updated location information of Dandelion. is the current dandelion location information, is a random number, and ; While using the Logistic-sin-cos chaotic map to generate the initial solution, the mirror reverse learning strategy is introduced. The expression of the mirror reverse learning strategy is: ， ， In the formula, To use the mirror reverse learning to update the reverse dandelion position information, is the upper limit of the current problem, is the lower bound of the current problem, is the dimension of the population, , , r are random numbers uniformly distributed between 0 and 1. Indicates the multiplication sign; Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the ascending stage, descending stage and landing stage in the improved dandelion optimization algorithm; The Cauchy spiral inverse cumulative distribution mutation strategy is started to mutate the array element position, and the target minimum peak sidelobe level of the updated array element distribution is calculated. If the value of the target minimum peak sidelobe level is greater than the value of the minimum peak sidelobe level, the updated array element distribution is replaced; Update the global optimal array element distribution position and the corresponding minimum peak sidelobe level; Determine whether the termination condition is met. If not, update the number of iterations and iterate. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

2. The multi-strategy joint improved sparse array optimization method according to claim 1, characterized in that: The updating of the array element positions according to the ascending phase, descending phase and landing phase in the improved dandelion optimization algorithm comprises: The analytical formula for dandelion seeds in the rising stage is: ， ， ， ， ， ， ， ， In the formula, For the t The location information of the dandelion group in the iteration, For the t +1 iteration of the location information of the dandelion group, To adjust the adjustment factor of the local search area, is a random number between 0 and 1, q is an adaptive parameter, lnY means obeying μ=0, = 1, is the mean, is the variance, is the maximum number of iterations, and represents the lift component coefficient of the dandelion seed in the horizontal and vertical directions, is the adaptive step size factor, is the upper limit of the current problem, is the lower limit of the current problem, Dim represents the dimension of the problem, is a random number between (-π, π), is a randomly selected position in the search space in the tth iteration.

3. The multi-strategy joint improved sparse array optimization method according to claim 1, characterized in that: The updating of the array element positions according to the ascending phase, descending phase and landing phase in the improved dandelion optimization algorithm also includes: In the descending stage, the mathematical model of dandelion seeds is expressed as: ， ， ， In the formula, is the average position information of the dandelion population at the tth iteration, is a random number, which obeys the Brownian motion of normal distribution, is the number of dandelion populations, is the current dandelion location information, For the t The location information of the dandelion group in the iteration, For the t +1 iteration of the location information of the dandelion group, is the maximum number of iterations.

4. The multi-strategy joint improved sparse array optimization method according to claim 1, characterized in that: The updating of the array element positions according to the ascending phase, descending phase and landing phase in the improved dandelion optimization algorithm comprises: During the landing phase, according to the selection probability The update strategy for determining the dandelion target position is expressed as: ， ， In the formula, To adjust the adjustment factor of the local search area, is the upper limit of the current problem, is the lower bound of the current problem, is the current dandelion location information, The updated location information of Dandelion. is the optimal dandelion location information, is the Levy flight function, is a linear increasing function, with a value of [0,2]. is the adaptive step size factor; The expression for calculating the selection probability is: ， In the formula, is the current iteration number, is the maximum number of iterations.

5. The multi-strategy joint improved sparse array optimization method according to claim 1, characterized in that: in, The expression of the Cauchy spiral inverse cumulative distribution mutation strategy is: ， ， ， In the formula, For the t +1 iteration of the location information of the dandelion group, is the optimal dandelion location information, For the t The location information of the dandelion group in the iteration, is a uniformly distributed random number in [−1,1], is the probability density function of the inverse Cauchy distribution, is 0, is 1, is a random number between [0,1], It consists of an exponential function based on e and varies with the number of iterations. is the spiral flight step length, the value is 1, is the current iteration number, is the maximum number of iterations.

6. A multi-strategy joint improved sparse array optimization system, characterized in that: include: An initialization module is configured to initialize the array element position according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution, wherein the expression of the Logistic-sin-cos chaotic map in the Logistic-sin-cos chaotic mirror reverse learning strategy is: ， In the formula, The updated location information of Dandelion. is the current dandelion location information, is a random number, and ; While using the Logistic-sin-cos chaotic map to generate the initial solution, the mirror reverse learning strategy is introduced. The expression of the mirror reverse learning strategy is: ， ， In the formula, To use the mirror reverse learning to update the reverse dandelion position information, is the upper limit of the current problem, is the lower bound of the current problem, is the dimension of the population, , , r are random numbers uniformly distributed between 0 and 1. Indicates the multiplication sign; A first updating module is configured to calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the ascending stage, descending stage and landing stage in the improved dandelion optimization algorithm; A calculation module is configured to start the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the array element position, calculate the target minimum peak sidelobe level of the updated array element distribution, and replace it with the updated array element distribution if the value of the target minimum peak sidelobe level is greater than the value of the minimum peak sidelobe level; A second updating module is configured to update the global optimal array element distribution position and the corresponding minimum peak sidelobe level; The output module is configured to determine whether the termination condition is met, if not, update the number of iterations and iterate, otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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