关键词: 多目标优化, 粒子群优化, 局部搜索, 全局最优解, 局部最优解

Abstract: To keep the diversity of particles when multi-objective particle swarm optimization is running, a multi-objective optimization algorithm was proposed based on particle swarms initialization and dynamic multiple particle swarms cooperation. The quantity of swarms was increased or decreased dynamically according to the distribution of particle swarms in the decision space. To avoid converging too quickly, the factors, which affected the flying speed of a particle, were improved to depend on the current velocity inertia of the particle, the best value of the particle, the best value of the swarm which the particle belonged to, and the optimal value of all swarms. This algorithm was tested by five benchmark functions and compared with the multi-objective particle swarm optimization. The experimental results indicate that the proposed algorithm is superior to the multi-objective particle swarm optimization.

Key words: Multi-Objective Optimization (MOO), Particle Swarm Optimization (PSO), local search, global optimal solution, local optimal solution