Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6857397 | Information Sciences | 2016 | 21 Pages |
Abstract
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has shown a superior performance in tackling some complicated multiobjective optimization problems (MOPs). However, the use of different evolutionary operators and their various parameter settings has a significant impact on its performance. To enhance its algorithmic robustness and effectiveness, this paper proposes an adaptive composite operator selection (ACOS) strategy for MOEA/D. Four evolutionary operator pools are used in ACOS and their advantages are combined to provide stronger exploratory capabilities. Regarding each selected operator pool, an online self-adaptation for the parameters tuning is further employed for performance enhancement. When compared with other adaptive and improved strategies designed for MOEA/D, our proposed algorithm is found to be effective and competitive in solving several complicated MOPs.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qiuzhen Lin, Zhiwang Liu, Qiao Yan, Zhihua Du, Carlos A. Coello Coello, Zhengping Liang, Wenjun Wang, Jianyong Chen,