Journal
INFORMATION SCIENCES
Volume 339, Issue -, Pages 332-352Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2015.12.022
Keywords
Adaptive composite operator selection; Adaptive parameters tuning; Differential evolution; Decomposition
Categories
Funding
- National Natural Science Foundation of China [61402291, 61170283]
- National High-Technology Research and Development Program (863 Program) of China [2013AA01A212]
- Ministry of Education in the New Century Excellent Talents Support Program [NCET-12-0649]
- Foundation for Distinguished Young Talents in Higher Education of Guangdong [2014KQNCX129]
- Shenzhen Technology Plan [JCYJ20140418095735608]
- Natural Science Foundation of SZU [201531]
- CONACyT [221551]
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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. (C) 2015 Elsevier Inc. All rights reserved.
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