关键词: 微粒群优化, 多目标优化问题, 多目标分解, 协同进化

Abstract: Particle Swarm Optimization (PSO) algorithm is a very competitive swarm intelligence algorithm for multi-objective optimization problems. Because it is easy to fall into local optimum solution, and the convergence and accuracy of Pareto set are not satisfactory, so the paper proposed a multi-objective particle swarm optimization algorithm of multi-swarm co-evolution based on decomposition (MOPSO_MC). In the proposed algorithm, each sub-swarm corresponded to a sub-problem decomposed by multi-objective decomposition method, and the authors constructed a new update strategy for the velocity. Each particle followed its own previous best position, sub-swarm best position and sub-swarm neighborhood best position, which resulted in enhancing the ability of local searching and getting evolutionary information from the neighborhood sub-swarm. Finally, the simulation results verify the convergence of the proposed algorithm, and the uniformity and correctness of the solution distribution with comparison of the state-of-the-art multi-objective particle swarm algorithm on ZDT test function.

Key words: Particle Swarm Optimization (PSO), multi-objective optimization problem, multi-objective decomposition, co-evolution