Improved Multi-objective Particle Swarm Optimization Algorithm For The Synthesis Of Antenna Arrays

Most of the problems encountered in actual scientific research and production life belong to multi-objective optimization problems,involving various application fields such as civil and bridge construction,power distribution,antenna optimization,etc.,and significant research results have been achieved in related fields.Although the multi-objective particle swarm optimization algorithm has the advantages of fewer adjustment parameters,simple structure and easy to understand and use,the search accuracy of the algorithm is still poor when dealing with complex problems or requiring a large amount of data computation,and there are many shortcomings such as easily falling into local optimal solutions.In this thesis,the multi-objective particle swarm optimization algorithm is studied in depth,and reasonable improvements are made to address the existing defects.First of all,in order to solve the shortcoming of low local search accuracy in the late stage of the algorithm,a new dynamic nonlinear cosine variable inertia parameter is proposed,which can not only meet the global search requirements in the early stage,but also make the algorithm have higher search accuracy in the late stage.Secondly,based on the Pareto dominance strategy,a dominance comparison method that can adapt to more targets is proposed,which can not only avoid blindly selecting the best individual,but also greatly reduce the amount of calculation.Then,a crowded distance strategy is used to maintain the external archives for problems such as uneven distribution of solution sets,and a roulette wheel is used to select the global optimum in the external archives.Finally,this thesis integrates the two algorithms of K-means clustering and Gaussian mutation into the optimization process of particle swarm optimization.The clustering algorithm is first applied to classify the population,and then the variance in each class is used to apply Gaussian variation to the individuals in this class.This strategy is equivalent to a tiny perturbation of each dimension on the basis of the original solution,which not only enables the algorithm to overcome the defects such as falling into local optimal solutions,but also greatly improves the search accuracy of the algorithm.By optimizing the ZDT(Zitzler-Deb-Thiele)and DTLZ(Deb-Thiele-Laumanns-Zitzler)series multi-objective test functions.The mean and variance of each evaluation index are calculated,and the distribution of Pareto solution sets are plotted and compared with other five common algorithms.The experimental results show that the solution set obtained by the improved algorithm in this thesis is significantly more convergent and the solution set distribution is more uniform,which indicates that the improved algorithm in this thesis has better performance in the field of multi-objective optimization.Finally,the improved particle swarm optimization algorithm is applied to the optimization problems of complex antennas such as one-dimensional linear arrays,two-dimensional planar arrays,and three-dimensional cylindrical arrays.And the design goals of narrow main lobe and low side lobe of the antenna pattern are also achieved,and the performance of the antenna is significantly better than other algorithms.And for the optimization problem of conformal array,which is difficult to optimize by traditional methods,the improved algorithm in this thesis has also achieved satisfactory results.The experimental results show that the improved algorithm in this thesis has certain practicability in the field of actual scientific research and production and life.