Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning
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 ad...
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| Published in: | Knowledge-based systems Vol. 277; p. 110801 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
09-10-2023
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 110801 |
| Author | Mei, Changrong Deng, Changshou Wu, Zhijian Cao, Lianglin Wang, Hui Peng, Hu Han, Yupeng |
| Author_xml | – sequence: 1 givenname: Yupeng surname: Han fullname: Han, Yupeng organization: School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, PR China – sequence: 2 givenname: Hu orcidid: 0000-0003-3381-3246 surname: Peng fullname: Peng, Hu email: hu_peng@whu.edu.cn organization: School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, PR China – sequence: 3 givenname: Changrong surname: Mei fullname: Mei, Changrong organization: School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, PR China – sequence: 4 givenname: Lianglin surname: Cao fullname: Cao, Lianglin organization: School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, PR China – sequence: 5 givenname: Changshou surname: Deng fullname: Deng, Changshou organization: School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, PR China – sequence: 6 givenname: Hui orcidid: 0000-0001-8213-1626 surname: Wang fullname: Wang, Hui organization: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China – sequence: 7 givenname: Zhijian surname: Wu fullname: Wu, Zhijian organization: School of Computer Science, Wuhan University, Wuhan 430072, PR China |
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| Keywords | Differential evolution Multistrategy Reference point adaptation Multiobjective optimization Reinforcement learning |
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| SubjectTerms | Differential evolution Multiobjective optimization Multistrategy Reference point adaptation Reinforcement learning |
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