Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm

被引：60
|
作者：
Lin, Qiuzhen [1 ]
Liu, Zhiwang [1 ]
Yan, Qiao [1 ]
Du, Zhihua [1 ]
Coello, Carlos A. Coello [2 ]
Liang, Zhengping [1 ]
Wang, Wenjun [1 ]
Chen, Jianyong [1 ]
机构：
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
[2] CINVESTAV, IPN, Dept Comp Sci, Mexico City 07360, DF, Mexico
来源：
INFORMATION SCIENCES | 2016年 / 339卷
基金：
中国国家自然科学基金;
关键词：
Adaptive composite operator selection; Adaptive parameters tuning; Differential evolution; Decomposition; IMMUNE ALGORITHM; OPTIMIZATION; DECOMPOSITION; MOEA/D; PERFORMANCE; PROXIMITY; DIVERSITY; ENSEMBLE; BALANCE; VERSION;
D O I：
10.1016/j.ins.2015.12.022
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：332 / 352
页数：21
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