Performance analyzing and researching of improved PSO algorithm

To deal with the slow search speed,premature convergence and lower search performance and individual optimizing ability in late stage,this paper proposed a new PSO called genetic PSO.Produced mutation and crossover of GA into velocity and position updating of PSO.The mutation to velocity could reduce the possibility of the algorithm trapping in the local optimal because of the over dense of the population in late stage.The crossover to position could make the gene of excellent elder individuals passed down to the next generation,and by doing so,attained the more excellent and more various next generations,so increased the evolution and search performance of the population.Selected several other typical improved PSO algorithms for comparing and analyzing from implementing process,setting of parameters and optimization performance.To simulated annealing PSO,proposed a new annealing method which could increase the speed of implementation of the algorithm.The simulation experiments were done to the six selected Benchmark functions.The results show that the proposed algorithm not only speeded up the convergence,but also improved the search performance in late stage and could converge to the global optimal solution more efficiently.And lastly,presented the simulation of dynamic optimizing process of genetic PSO to the Griewank function so that converging process of the particles could be viewed vividly.