Research On Improved Particle Swarm Optimization Algorithm In Static Environmen

Swarm intelligence algorithms show strong adaptability in solving high dimensional optimization problems,among which Particle Swarm Optimization(PSO)has been widely concerned because of its strong global search ability and simple principle.However,it has an obvious disadvantage that it is easy to fall into local convergence.Therefore,in order to solve this dilemma,the PSO in the static environment is improved in this paper,and its contents are as follows:(1)The PSO under the single objective optimization is improved.Due to the lack of local search ability of PSO in this situation,the solutions that fall into the convergence state are recorded through marker factors,and then the population structure is changed,combined with learning strategies to improve the local search ability of the algorithm.Through numerical experiment,the above measures can improve the performance of PSO.(2)The PSO under the multi-objective optimization is improved.In this case,the algorithm has insufficient ability to jump out of local convergence,and the solution results are heavily dependent on the prior knowledge of particles.Therefore,archival differentiation is needed,and then update learning mechanism is added to enhance the search for the optimal region.Through numerical experiment,the above strategy can improve the diversity of particles and the solution accuracy of the algorithm.(3)Although archival differentiation can improve the diversity of particles,the algorithm results are heavily dependent on the strategies adopted during archival differentiation.Therefore,on the basis of the above paragraph,it is necessary to apply multiple archives to store high-quality particles,and then adopt the idea of Gaussian perturbation to improve the quality of the solution set of the algorithm.Through numerical experiment,the above strategy can improve the particle distribution and convergence.(4)In order to explore whether the elimination of restrictions on archiving and population size will affect the solution results of the algorithm.Firstly,particles are selected by clustering.Secondly,the adaptive strategy is used to expand or reduce the population size,so as to eliminate the archiving and population size restriction.Through numerical experiment,the above strategies can ensure the distribution and convergence of particles while eliminating the limitation of archiving and population size.