文献类型：期刊文献

英文题名：A decomposition-based multiobjective evolutionary algorithm with weight vector adaptation

作者：Zhou, Xin[1];Wang, Xuewu[1];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2021

卷号：61

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20210209752043);WOS:【SCI-EXPANDED(收录号:WOS:000651800900001)】；

基金：This work is supported by the National Natural Science Foundation of China (No. 61973120 , 62076095 , 61673175 , 61573144) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 , and Fundamental Research Funds of the Central Universities , 222201917006 .

语种：英文

外文关键词：Adaptive weight vector; Environment selection mechanism; MOEA; D; Neighborhood adaptation

摘要：Multi-objective Evolutionary Algorithms (MOEAs) have been concerned and studied with great achievements in the last two decades. As a typical decomposition-based MOEA, MOEA/D aims to decompose a multi-objective op-timization problem (MOP) into several subproblems through a set of predefined weight vectors and then optimizes these problems simultaneously. However, performance degradation occurs when complex optimization problems with complicated Pareto Front shape (i.e., irregular and discontinuous PF) are handled. This paper proposes a decomposition-based multi-objective evolutionary algorithm with weight vector adaptation (WVA-MOEA/D) to adjust the weight vectors uniformly distribute in the solution space. The algorithm decomposes a MOP into sev-eral subproblems, the new environment selection mechanism defines several neighborhoods with weight vectors as the center of the circle, and elite solutions are selected based on the density of each neighborhood. Weight vector adaptation is employed to guide solution selection and obtain a set of uniformly distributed solutions. The proposed WVA-MOEA/D can improve the performance of MOEA/D on MOPs and many-objective problems with irregular PFs. Besides, the neighborhood adaptation strategy used in the algorithm aims to maintain the diversity solutions and decrease the selection pressure in many-objective optimization problems. Experimental results indicate that WVA-MOEA/D could further effectively solve MOPs with various types of Pareto Fronts for multi-objective and many-objective optimization compared with several state-of-the-art evolutionary algorithms.