Research And Application Of Particle Swarm Optimization Algorithm

Particle swarm optimization algorithm(P SO)is a typical example of group intelligent algorithm.It has the advantages of easy implementation,simple principle and few parameters.At present,the algorithm has been widely used in many fields,such as image processing,engineering optimization,data control and other related fields.Since the particle swarm algorithm is a group of intelligent algorithms derived from biological groups,it has some shortcomings in the application and theoretical basis,and it needs to be further studied.The main contents are as follows:(1)The Particle Swarm Optimization Algorithm has a fast convergence speed,but it is easy to fall into the local optimum;Fireworks explosion algorithm(FEO)has a strong global search ability,but it also has shortcomings,such as not accurate solutions,low efficiency.In order to solve these two problems,this paper introduces the fireworks explosion algorithm into the PSO algorithm,and proposes a new FEPSO(Fireworks Explosion Particle Swarm Optimization Algorithm)joint algorithm,which improves the explosion radius of FEO,so as to improve the explosion radius of FEO.The joint algorithm can converge quickly to the global optimum,and the precision can be greatly improved.Firstly,the population is dynamically divided into several small populations by using the median clustering algorithm,and the optimal solution is searched by parallel processing;then the optimal particles selected in each small population are taken as the initial population of FEO depth optimization;finally,the convergence of the proposed fusion theory to prove the theoretical and experimental analysis.The results show that the proposed algorithm has obvious effect on the convergence accuracy of the lifting function compared with other algorithms,especially for the multi-peak function.(2)As we all know,traditional particle swarm optimization algorithm is easy to fall into local optimum and has slow convergence,low accuracy and some other disadvantages.To overcome the disadvantages,a fusion classification and expansion strategies-based particle swarm optimization algorithm is proposed.The algorithm uses a classification optimization strategy to eliminate inferior solutions for the particle swarm which falls into local optimum;it adopts a expansion strategy to generate high quality solutions to improve the convergence precision of particle swarm optimization algorithm;meanwhile,a normal evolutionary mutation strategy is presented to search the optimal particle neighborhood space,since this may enhance the capacity of local exploration and avoid the algorithm trapping into local optimum.The experiments are conducted on 6 classic functions to find the minimum issue using intelligent search algorithm,and the results show that the improved algorithm is better than the recent improved particle swarm optimization algorithm inconvergence speed,especially in multimodal function is more outstanding.Finally,the fusion algorithm is applied to the test method of hydrological frequency and the test is carried out.The results show that the fusion algorithm is more accurate in estimating the parameters than the other methods.It is a good tool in the use of the hydrological frequency analysis.