Research of immune particle swarm optimization algorithm based on Gaussian distribution and simulated annealing algorithm
基于高斯分布和模拟退火算法的免疫微粒群优化算法研究

The particle swarm optimization algorithm is not only easy to lose diversity and run into local optimization in course of search, but also the speed of search is low. This article presented an immune Particle Swarm Optimization (PSO) algorithm through immune inoculation and immune choice, which recurs to mechanism of GUASS and SA. The common norm function is used to develop simulated and validated work. Comparison of simulated results between Immune Particle Swarm Optimization (IPSO), PSO and DWIPSO shows that IPSO has the advantage of improving the global search ability and decreasing calculated steps.