Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm

作者：Lin, Qiuzhen; Liu, Zhiwang; Yan, Qiao; Du, Zhihua^*; Coello, Carlos A. Coello; Liang, Zhengping; Wang, Wenjun; Chen, Jianyong

来源：Information Sciences, 2016, 339: 332-352.

DOI：10.1016/j.ins.2015.12.022

• Adaptive composite operator selection
• Adaptive parameters tuning
• Differential evolution
• Decomposition

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.

• 出版日期2016-4-20
• 单位深圳大学