关键词: 元胞模型, 多策略差分进化, 多目标优化, 拥挤距离评估, 替换策略

Abstract: To improve the convergence and distribution of Multi-Objective Evolutionary Algorithms (MOEAs) on solving high dimensional multi-objective optimization problems (MOPs),a cellular multi-objective genetic algorithm based on multi-strategy differential evolution was proposed.Through analyzing the different differential evolution strategies,a selection operator of multi-strategy differential evolution based on the cellular model was proposed.Aiming at the defects of the present crowding diversity measure,a new crowding diversity measure method based on entropy was introduced and the replacement policy was also improved.Through testing 12 benchmark functions,the proposed algorithm was proved to have better convergence and diversity than NSGA-Ⅱ,MOCell and CellDE,and the coverage rate for solution was also improved,which especially for solving high dimensional multi-objective optimization problems.

Key words: cellular model, multi-strategy differential evolution, multi-objective optimization, crowding diversity measure, replacement policy