Application Of Particle Swarm Optimization Algorithm In Sparse Array Pattern Synthesis

Dust array antenna has a prominent advantage not only in the main lobe beam,sidelobe,zero and other patterns,but also reduces the construction cost and complexity of the antenna system.Pattern synthesis of array antennas refers to making radiation pattern approach the desired pattern as far as possible through determination of several parameters of antenna arrays.some practical projects need lower sidelobe level,narrower main lobe width,or in a specified location with deep zero and other patterns.Based on this,the study of more efficient particle swarm algorithms are applied to the dilute array pattern,which has important practical significance and application value.This paper aims to explore the theories and features of Particle Swarm Optimization algorithm,to design modified Particle Swarm Optimization algorithms for different synthesis problems,and to do the corresponding numerical experimental analyses.The main research works in this dissertation consist of the following aspects:(1)Combining particle swarm optimization with genetic algorithm,I present a hybrid particle swarm optimization algorithm.In this paper,the characteristic of hybrid mutation in genetic algorithm are introduced into the particle swarm optimization algorithm,and changes the diversity of particle population,so that the particle can jump out of the local optimal solution,to find the global optimal solution,and improve the ability of the searching and change the performance of the algorithm.(2)Combining particle swarm optimization with simulated annealing algorithm,I present an annealing particle swarm algorithm.In this paper,we use the method of simulated annealing algorithm to initialize the initial value of the particle swarm algorithm,so that the initial population of particle swarm algorithm can cover the whole search space uniformly,which avoids the phenomenon that the traditional initialization method is easy to gather to the edge when solving the high dimensional space optimization problem.This method is beneficial to the optimization of particle swarm optimization in high dimensional space.At the same time,the idea of the simulated annealing is introduced into the particle swarm optimization algorithm.Combining with the fast searching ability of particle swarm optimization and the probabilistic sudden jump characteristic of simulated annealing,the algorithm can jump out of local optimum to achieve global optimal and achieve better convergence accuracy(3)In order to make the particle swarm optimization algorithm more effectively solve the problem of the sparse array pattern.Particle swarm algorithm absorbs the advantages of chaotic algorithm,I present a chaotic particle swarm optimization algorithm.In this paper,we first use the chaotic sequence to initialize the velocity and position of the particles and improve the ergodicity of the whole population search.Secondly,based on the optimal position searched by the current population,I can get the chaotic sequence.The newly generated optimal position replaces the position of one of the particles in the current population.If the algorithm introduces into the chaotic sequence,it can generate many neighborhood points of the local optimal solution in the evolution process,to help the inert particles escape the local minimum points and quickly search the optimal solution and improve the searching ability of the algorithm.(4)In this paper,the improved particle swarm optimization algorithms are used to solve the different problems of array antenna pattern.In order to verify the performance of the algorithm,the hybrid particle swarm optimization algorithm is applied to the design of two typical arrays,and the solution results are compared with the optimal solution of the particle swarm optimization algorithm and the genetic algorithm.The accuracy and speed of the method are better than the particle swarm algorithm and the genetic algorithm.In this paper,the annealing particle swarm optimization algorithm is applied to the dip pattern of the dilute array.The dilute array is designed with a deep zero point in a specified position.Through the iterative optimization of the algorithm,a better array of elements is obtained.And Comparing with the results of other algorithms,I find that annealed particle swarm algorithm has a great advantage.In this paper,the chaotic particle swarm optimization algorithm is applied to the sparse array pattern,and the sparse linear array is designed by using the chaos particle swarm optimization algorithm.And comparing the results of existing literatures,it shows that chaos particle swarm algorithm has a great advantage in solving such problems.