文献类型：期刊文献

英文题名：An adaptive decomposition-based evolutionary algorithm for many-objective optimization

作者：Han, Dong[1];Du, Wenli[1];Du, Wei[1];Jin, Yaochu[1,2];Wu, Chunping[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England;[3]Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：491

起止页码：204

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20191506746929);WOS:【SCI-EXPANDED(收录号:WOS:000468717100014)】；

基金：This work was supported by National Natural Science Foundation of China (Major Program: 61590923), National Science Fund for Distinguished Young Scholars (61725301), the Fundamental Research Funds for the Central Universities and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Evolutionary multi-objective optimization; Many-objective optimization; Decomposition; Convergence; Diversity; Penalty boundary intersection; Adaptation

摘要：Penalty boundary intersection (PBI) is one popular method in decomposition based evolutionary multi-objective algorithms, where the penalty factor is crucial for striking a balance between convergence and diversity in a high-dimensional objective space. Meanwhile, the distribution of the obtained solutions highly depends on the setting of the weight vectors. This paper proposes an adaptive decomposition-based evolutionary algorithm for many-objective optimization, which introduces one adaptation mechanism for PBI-based decomposition and the other for adjusting the weight vector. The former assigns a specific penalty factor for each subproblem by using the distribution information of both population and the weight vectors, while the latter adjusts the weight vectors based on the objective ranges to handle problems with different scales on the objectives. We have compared the proposed algorithm with seven state-of-the-art many-objective evolutionary algorithms on a number of benchmark problems. The empirical results demonstrate the superiority of the proposed algorithm. (C) 2019 Elsevier Inc. All rights reserved.