An improved MOEA/D algorithm with an adaptive evolutionary strategy

The Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) overcomes the limitation of evolutionary algorithm based on a Pareto dominant relationship in dealing with the problem of super multi-objective optimization and has wide application prospects, but there are also some problems, such as the lack of diversity and slow convergence speed in the later-stage evolution species. This article specifically conducts a systematic study on the population diversity of the MOEA/D algorithm and proposes three improvements: firstly, the evolutionary strategy of competition between SBX and DE operator is adopted to overcome the problem of the species diversity degradation of a single operator; secondly, an adaptive adjusting strategy of modulation probability is introduced to promote the variability of later-stage evolution species; finally, a method of double-faced mirror theory boundary optimization is used to prevent species aggregating at the boundary. The research shows that the above three improvement measures can effectively improve the population diversity of the MOEA/D algorithm. On the basis of this research, an improved MOEA/D algorithm with adaptive evolution strategy (AES-MOEA/D) is proposed. Simulation experiment indicators show that the convergence and comprehensive performance of the AES-MOEA/D algorithm are better than that of the basic MOEA/D algorithm and the other four comparison algorithms, which shows that the research on the maintenance of the diversity of the MOEA/D algorithm population helps improve the comprehensive performance of the MOEA/D algorithm. (C) 2020 Elsevier Inc. All rights reserved.