Multi-strategy joint improved sparse area array optimization method and system

Abstract

The invention discloses a multi-strategy joint improved sparse area array optimization method and system, and the method comprises the steps: calculating the peak side lobe level of each initial array element distribution, and obtaining the current lowest peak side lobe level and the optimal array element distribution corresponding to the lowest peak side lobe level, updating the positions of the array elements according to an ascending stage, a descending stage and a landing stage in the improved dandelion optimization algorithm; starting a Cauchy spiral inverse cumulative distribution variation strategy to vary the positions of the array elements, calculating the target lowest peak side lobe level of the updated array element distribution, and if the value of the target lowest peak side lobe level is greater than the value of the lowest peak side lobe level, replacing the updated array element distribution; and updating the global optimal array element distribution position and the corresponding lowest peak sidelobe level. The efficiency of the algorithm in the aspects of global search and local development is improved, the algorithm is prevented from being caught in local optimum too early, and therefore the solving precision and the convergence speed of the algorithm are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of array antenna processing, and in particular relates to a multi-strategy joint improved sparse array optimization method and system. Background Art

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

[0003] By using computer technology to model and identify the parameters of the array antenna, and then performing a global search through the optimization algorithm, the lowest peak sidelobe level can be obtained, thereby determining the optimal array antenna layout. The existing algorithms for array antenna optimization mainly include genetic algorithms, particle swarm algorithms, and invasive weed optimization algorithms. These swarm intelligence algorithms have been widely used in the field of industrial optimization production. However, the design of sparse laser array antennas is a complex nonlinear optimization problem. 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 take into account both global and local searches has become an important issue in array antenna optimization. Summary of the invention

[0004] The present invention provides a multi-strategy joint improved sparse array optimization method and system, which are used to solve the technical problem that array element parameters and distribution cannot be determined quickly and accurately.

[0005] In a first aspect, the present invention provides a multi-strategy joint improved sparse array optimization method, comprising: Initialize the array element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; 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.

[0006] In a second aspect, the present invention provides a multi-strategy joint improved sparse array optimization system, comprising: An initialization module, configured to initialize array element positions according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; 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.

[0007] According to a third aspect, an electronic device is provided, comprising: 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 so that the at least one processor can perform the steps of the multi-strategy jointly improved sparse array optimization method of any embodiment of the present invention.

[0008] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the processor to execute the steps of the multi-strategy joint improved sparse array optimization method of any embodiment of the present invention.

[0009] The multi-strategy jointly improved sparse array optimization method and system of the present application adopts the Logistic-sin-cos chaotic mirror reverse learning strategy to form a uniform and diverse dandelion initialization population to improve the global search capability of the algorithm; the adaptive step size strategy increases the probability of algorithm population mutation and the possibility of jumping out of the local optimum; the local mutation search strategy of integrating Cauchy inverse cumulative mutation and spiral flight is used to optimize the algorithm population, which improves the efficiency of the algorithm in global search and local development, avoids the algorithm from falling into the local optimum too early, thereby improving the solution accuracy and convergence speed of the algorithm, so that the sparse laser array antenna can be better optimized to ensure the minimization of the peak sidelobe level. Compared with the existing traditional technology, it has more superior global search capability and higher optimization accuracy, and provides a new method for sparse laser array antenna optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a multi-strategy joint improved sparse array optimization method provided by an embodiment of the present invention; Figure 2 A structural block diagram of a multi-strategy joint improved sparse array optimization system provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] Assume a sparse rate of , the array size is A rectangular planar sparse laser array with a grid spacing of , is the radiation wavelength, and the number of array elements is Assuming that it satisfies equal amplitude omnidirectionality, according to the principle of electromagnetic wave superposition, the directional pattern function of the array can be expressed as: , In the formula, is the radiation pattern function, for( m , n ) working status of the position element, for( m , n ) The amplitude excitation coefficient of the array element at position, is half the wavelength, is the conjugate complex number, = , is the pitch angle, is the azimuth, is the main lobe azimuth, is the main lobe pitch angle; With the goal of reducing the peak sidelobe level of the directivity pattern, under the condition of equal amplitude omnidirectionality, and The maximum sidelobe level is searched in two planes. According to the PSLL definition, the fitness function is expressed as: , In the formula, express The sidelobe interval of the azimuth pattern at time express The sidelobe interval of the elevation pattern when . The sparse laser array antenna optimization model can be defined as: , Indicates the minimum peak sidelobe level.

[0014] See also Figure 1 , which shows a flowchart of a multi-strategy joint improved sparse array optimization method of the present application.

[0015] like Figure 1 As shown, the multi-strategy joint improved sparse array optimization method specifically includes the following steps: Step S101 , initializing array element positions according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution.

[0016] In this step, the expression of the Logistic-sin-cos chaotic map in the Logistic-sin-cos (Logistic chaotic map, sin function, cos function) 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. Represents the multiplication sign.

[0017] Step S102, 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.

[0018] In this step, the Dandelion Optimization Algorithm simulates the position update and optimization of mature dandelions flying in the wind, and divides it into three stages: ascent, descent, and landing. The diverse position update strategies of the Dandelion Optimization Algorithm can explore the solution space more comprehensively, and show excellent performance in solving continuous and discrete optimization problems.

[0019] During the rising stage, dandelion seeds are affected by factors such as wind speed and air humidity. Therefore, they can be divided into sunny days and rainy days according to the weather.

[0020] On sunny days, the wind speed follows a log-normal 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 vortex above the dandelion seeds is constantly adjusted, showing a spiral upward shape. In this process, the expression corresponding to the seed rising stage is: , , , , , , , , In the formula, For the The location information of the dandelion group in the iteration, For the The location information of the dandelion group in the iteration, To adjust the adjustment factor of the local search area, is a random number between 0 and 1, q is an adaptive parameter, and lnY means obeying The lognormal distribution of 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, for A random number between is the randomly selected position in the search space in the tth iteration; On rainy days, due to factors such as air humidity and air resistance, dandelion seeds cannot fully rise with the wind. At this time, the dandelion optimization algorithm emphasizes local area development, and the expression is: , , , In the formula, To adjust the adjustment factor of the local search area, is a random number between 0 and 1, and q is an adaptive parameter.

[0021] The analytical expression describing the dandelion seeds in the rising stage is: , In the descending stage, the dandelion optimization algorithm emphasizes global optimization. The average position information after the ascending stage can not only reflect the stability of the dandelion's descent, but also promote the dandelion population to reproduce in the most ideal location. The corresponding mathematical model for the descending stage is as follows: , , 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 The location information of the dandelion group in the iteration, For the The location information of the dandelion group in the iteration.

[0022] In the landing phase, the dandelion optimization algorithm focuses on development. In order for the seeds to reach the position where they are most likely to survive, the current elite individual information can be used for local development, so that the algorithm can accurately converge to the global optimal solution, expressed as: , , In the formula, is the optimal dandelion location information, is the Levy flight function, which is used to enhance the local search capability. It is a linear increasing function with a value between [0,2], which is used to avoid over-development and make it converge accurately to the global optimum.

[0023] It should be noted that 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; Among them, according to the selection probability The update strategy for determining the target position of dandelions can perturb the dandelion population during the landing phase. Compared with most existing methods, this strategy has advantages in enhancing population diversity and escaping local optimality. The selection probability This allows the algorithm to focus on global search in the early stages and turn to jumping out of local optimality in the later stages, which is beneficial for ensuring accurate convergence after sufficient global search.

[0024] The expression for calculating the selection probability is: , In the formula, is the current iteration number, is the maximum number of iterations.

[0025] Step S103, starting the Cauchy spiral inverse cumulative distribution mutation strategy to mutate the array element position, calculating the target minimum peak sidelobe level of the updated array element distribution, and replacing 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.

[0026] In this step, the long-tail characteristic of the Cauchy inverse cumulative distribution helps the algorithm to jump out of the local optimum and enhance the global search capability. The spiral flight strategy further expands the search range and avoids falling into the local optimum too early. Compared with the existing improved strategy, the proposed method can better balance the effects of global search and local development and improve the convergence speed. The expression of the Cauchy spiral inverse cumulative distribution mutation strategy is: , , , In the formula, For the The location information of the dandelion group in the iteration, is the optimal dandelion location information, For the The location information of the dandelion group in the iteration, for A uniformly distributed random number, 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.

[0027] Step S104: updating the global optimal array element distribution position and the corresponding minimum peak sidelobe level.

[0028] Step S105, determining whether the termination condition is met, if not, updating the number of iterations and performing iterations, otherwise, outputting the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0029] In summary, the method of the present application, based on the existing Dandelion optimization algorithm, introduces the Logistic-sin-cos chaotic mirror reverse learning strategy and the local mutation search strategy to generate a more uniform and higher quality initial solution set, and by integrating the adaptive step size strategy, increases the probability of population mutation and improves the possibility of jumping out of the local optimum, while the local mutation search strategy adopts the 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 algorithm's global exploration ability and local optimal solution escape ability, and effectively accelerates the algorithm's convergence process. The improved Dandelion optimization algorithm is applied to the solution of the sparse laser array antenna pattern optimization model, which can quickly obtain the optimal array solution and ensure the minimization of the peak sidelobe level.

[0030] See also Figure 2 , which shows a structural block diagram of a multi-strategy joint improved sparse array optimization system of the present application.

[0031] like Figure 2 As shown, the multi-strategy joint improved sparse array optimization system 200 includes an initialization module 210, a first updating module 220, a calculation module 230, a second updating module 240 and an output module 250.

[0032] Among them, the initialization module 210 is configured to initialize the array element position according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; the first update module 220 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; the calculation module 230 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, if the value of the target minimum peak sidelobe level is greater than the value of the minimum peak sidelobe level, then replace it with the updated array element distribution; the second update module 240 is configured to update the global optimal array element distribution position and the corresponding minimum peak sidelobe level; the output module 250 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 minimum peak sidelobe level of each generation.

[0033] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0034] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the processor is caused to execute the multi-strategy joint improved sparse array optimization method in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: Initialize the array element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; 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.

[0035] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the multi-strategy joint improved sparse 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 disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the multi-strategy joint improved sparse array optimization system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3As shown, the device includes: a processor 310 and a memory 320. The electronic device may also 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 via a bus or other means. Figure 3 In the example, the bus connection is used. 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 the non-volatile software programs, instructions and modules stored in the memory 320, that is, the multi-strategy joint improved sparse array optimization method of the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the multi-strategy joint improved sparse array optimization system. The output device 340 may include display devices such as display screens.

[0037] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0038] As an implementation mode, the electronic device is applied to a multi-strategy joint improved sparse array optimization system, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: Initialize the array element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; 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.

[0039] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for 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 embodiment.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-strategy joint improved sparse array optimization method, characterized in that: include: Initialize the array element positions according to the preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; 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: in, The expression of the Logistic-sin-cos chaotic mapping 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. Represents the multiplication sign.

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 comprises: The analytical formula for dandelion seeds in the rising stage is: ， ， ， ， ， ， ， ， In the formula, For the The location information of the dandelion group in the iteration, For the The location information of the dandelion group in the iteration, To adjust the adjustment factor of the local search area, is a random number between 0 and 1, q is an adaptive parameter, and lnY means compliance The log-normal distribution of 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, for A random number between is a randomly selected position in the search space in the tth iteration.

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 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 The location information of the dandelion group in the iteration, For the The location information of the dandelion group in the iteration, is the maximum number of iterations.

5. 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.

6. 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 The location information of the dandelion group in the iteration, is the optimal dandelion location information, For the The location information of the dandelion group in the iteration, for A uniformly distributed random number, 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.

7. A multi-strategy joint improved sparse array optimization system, characterized in that: include: An initialization module, configured to initialize array element positions according to a preset Logistic-sin-cos chaotic mirror reverse learning strategy to generate a uniformly distributed initial array element distribution; 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.

8. 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 6.

9. 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 6 is implemented.

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