关键词: 多目标优化, 粒子群, 信息熵, 克隆选择

Abstract: In order to solve the problems of loss in diversity and poor distribution of Pareto solutions in Multi-Objective Particle Swarm Optimization（MOPSO）, a hybrid global best selecting strategy is proposed. Each particle’s global best is selected according to information entropy or Sigma value of solutions with a varying selecting probability. And clone selection strategy is used to update Pareto solution set according to dominance relationships. As a result, the better distributed solutions are exploited. Results on several benchmark functions show that the proposed algorithm has better distribution performance while maintains a good convergence. This indicates that the proposed hybrid strategy is effective in improving the diversity and distribution of MOPSO.

Key words: multi-objective optimization, particle swarm optimization, information entropy, clone selection