关键词: 粒子群算法, 多目标优化, 动态邻居, 最优维度粒子, 随机向导学习

Abstract: Focus on the poor behavior of the diversity for multi-objective particle swarm optimization and the selection pressure of population increasing with the variable dimension, a Multi-Objective Particle Swarm Optimization based on Dynamic Neighborhood of Dimensional Learning （DNDL-MOPSO） is proposed. Firstly, an optimum dimensional individual is established. Then based on the individual and social knowledge, the proposed algorithm improves the formula of the velocity updating and uses a strategy that each dimensional learning object is not fixed. Finally, the random guide learning strategy is used to alleviate the selection pressure. The experimental results indicate that the new algorithm can improve the global convergence and increase the diversity of population. It is effective to solve the benchmark multimodal optimization problems.

Key words: Particle Swarm Optimization（PSO）, multi-objective optimization, dynamic neighbor, optimum dimensional individual, random guide learning