关键词: Tent映射；混沌粒子群优化算法；群体适应度方差；多样性

Abstract:

In order to prevent getting into local best and appearing premature convergence in searching iterations of particle swarm optimization algorithm(PSO), the population fitness variance of particle swarm was used to describe and track the flying distribution state of particle swarm and to estimate particles whether being focusing or discrete. Furthermore it was used to estimate whether to have chaotic search. Based on the ergodicity, randomicity and disciplinarian of chaos as well as the advantages of Tent mapping, Tent mapping was used as a chaotic optimization searching and introduced into PSO to avoid PSO getting into local best and appearing premature convergence. This modified and novel PSO was called chaotic particle swarm optimization algorithm(CPSO).Typical test functions were used to test the performance of CPSO. And the testing results prove that CPSO is feasible. 

Key words: Tent mapping, chaotic particle swarm optimization algorithm(CPSO), population fitness variance;diversity