A Simple and Effective Immune Particle Swarm Optimization Algorithm

Abstract

AbstractThe Particle Swarm Optimization (PSO) algorithm is a population based evolutional search strategy, which has easer implementation and fewer presetting parameters. But the most difficulty of PSO having to encounter with is premature convergence. This is due to a decrease of diversity during the evolutional process that leads to plunging into local optimum and ultimately fitness stagnation of the swarm. In order to maintain appropriate diversity, a simple and effective immune PSO (IPSO) algorithm is proposed in the paper. IPSO takes advantage of immune operators to update the particles when the algorithm fails to converge to a given threshold. The most difference of IPSO here among other optimization algorithms with immunity is that Gaussian mutation is executed before selecting particles from immune memory library. So the diversity of population is extended adequately, and the risk of trapping into local optimum is depressed effectively. Testing over the benchmark problems, the experimental results indicate the IPSO algorithm prevents premature convergence to a high degree and has better convergence performance than Standard PSO algorithm.KeywordsParticle Swarm Optimizationimmune systemimmune memoryclonal selectionglobal searchdiversity