Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning

• Published in: Knowledge-based systems Vol. 277; p. 110801
• Main Authors: Han, Yupeng, Peng, Hu, Mei, Changrong, Cao, Lianglin, Deng, Changshou, Wang, Hui, Wu, Zhijian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 09-10-2023
• Subjects: Differential evolution; Multiobjective optimization; Multistrategy; Reference point adaptation; Reinforcement learning; Differential evolution; Multistrategy; Reference point adaptation; Multiobjective optimization; Reinforcement learning
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2023.110801

Multiobjective evolutionary algorithms (MOEAs) have gained much attention due to their high effectiveness and efficiency in solving multiobjective optimization problems (MOPs). However, when solving MOPs, it is important but difficult to maintain a good balance of exploration and exploitation. In addition, some reference point based MOEAs with fixed reference points perform poorly on MOPs with irregular frontiers. Therefore, this paper proposes a new multistrategy multiobjective differential evolutionary (DE) algorithm, named RLMMDE. In RLMMDE, a multistrategy and multicrossover DE optimizer is utilized to alleviate the exploration and exploitation dilemma. An adaptive reference point activation mechanism based on RL is proposed to activate the adaptive adjustment of reference points. Moreover, a reference point adaptation method is proposed to improve the performance of RLMMDE on irregular frontier problems. Experimental results of RLMMDE tested on some benchmark test suites (i.e., ZDT, DTLZ, UF, WFG, and LSMOP) and two practical mixed-variable optimization problems show that the algorithm outperforms some advanced MOEAs.