关键词: 高维多目标优化, 微分进化算法, 放松支配, 协同进化, 变异

Abstract: In order to improve the convergence and diversity of many-objective evolutionary algorithms, a many-objective differential evolution algorithm using a relaxed dominance relation is proposed. In the proposed algorithm, a relaxed domination relation is designed and incorporated to increase the selection pressure of individuals. Population is coevolved with an external archive, and the child population is generated by the mixed differential mutation operators. The fitness of each individual is evaluated based on an indicator method, and the population is updated. The archive is updated according to the Lp norm （0<p<1） distance based diversity maintenance strategy. In order to validate the effectiveness of the proposed algorithm in many-objective optimization, experiments in a set of state-of-the-art benchmarks are carried out. Compared with two other classical algorithms, the proposed algorithm can generate a set of non-dominated solutions with better convergence and distribution in many-objective optimization problems.

Key words: many-objective optimization, differential evolution algorithm, relaxed dominance, co-evolutionary, mutation