A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

作     者：Qiao, Junfei Zhou, Hongbiao Yang, Cuili Yang, Shengxiang 

作者机构：Beijing Univ Technol Coll Automat Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England 

出 版 物：《APPLIED SOFT COMPUTING》 (应用软计算)

年 卷 期：2019年第74卷

页      面：190-205页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation for Distinguished Young Scholars of China State Key Program of National Natural Science of China 

主　　题：Multiobjective evolutionary algorithm Decomposition Penalty boundary intersection Angle-based adaptive penalty 

摘      要：A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. In MOEA/D, decomposition mechanisms are used to push the population to approach the Pareto optimal front (POF), while a set of uniformly distributed weight vectors are applied to maintain the diversity of the population. Penalty-based boundary intersection (PBI) is one of the approaches used frequently in decomposition. In PBI, the penalty factor plays a crucial role in balancing convergence and diversity. However, the traditional PBI approach adopts a fixed penalty value, which will significantly degrade the performance of MOEA/D on some MOPs with complicated POFs. This paper proposes an angle-based adaptive penalty (AAP) scheme for MOEA/D, called MOEA/D-AAP, which can dynamically adjust the penalty value for each weight vector during the evolutionary process. Six newly designed benchmark MOPs and an MOP in the wastewater treatment process are used to test the effectiveness of the proposed MOEA/D-AAP. Comparison experiments demonstrate that the AAP scheme can significantly improve the performance of MOEA/D. (C) 2018 Elsevier B.V. All rights reserved.