关键词: 粒子群优化, 速度矢量, 动态Zaslavskii混沌映射公式, 动态非线性函数, 协同进化

Abstract: To solve the premature convergence problem of Particle Swarm Optimization (PSO), two new methods were introduced to improve the performance cooperatively: When particles’ fitness values were worse than the average, the dynamic Zaslavskii chaotic map formula was devised to modify the inertia weight and velocity, which can make particles break away from the local best and search the global best dynamically; On the contrary, when fitness values were better than or equal to the average, the introduced dynamic nonlinear functions were used to modify the inertia weight and velocity, which can make particles retain favorable conditions and converge to the global best continually. Two methods coordinate dynamically, and make two dynamic swarms cooperate to evolve. Experimental results demonstrate that the new introduced algorithm outperforms several other famous improved PSO algorithms on many well-known benchmark problems.

Key words: Particle Swarm Optimization (PSO), velocity, dynamic Zaslavskii chaotic map formula, dynamic nonlinear function, cooperative evolution