DLEA: A dynamic learning evolution algorithm for many-objective optimization
For many-objective problems, how to maintain the diversity and convergence of the distribution of the solution set over Pareto front (PF) has always been the research emphasis. In the iteration process, the state of population is critical to improve the level of evolution. Therefore, this paper will...
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| Vydané v: | Information sciences Ročník 574; s. 567 - 589 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.10.2021
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | For many-objective problems, how to maintain the diversity and convergence of the distribution of the solution set over Pareto front (PF) has always been the research emphasis. In the iteration process, the state of population is critical to improve the level of evolution. Therefore, this paper will use two convergence and diversity indicators to further strengthen the usage of evolutionary state information and propose a dynamic learning strategy. In addition, a dynamic learning strategy based many-objective evolutionary algorithm (MaOEA) is proposed, called dynamic learning evolution algorithm (DLEA), which continuously changes the direction of learning: convergence and diversity in the iteration process. The purpose is to make the algorithm prefer to convergence in the early iteration and prefer to diversity when it is close to PF in the late iteration, so that the convergence and diversity of the final solution set can be well maintained. And then, the performance of DLEA is measured by two indicators. Meanwhile, DLEA will be compared with four state-of-the-art algorithms on the DTLZ and MaF, and its performance will be verified on a many-objective combinatorial problem. And the experimental results and Friedman test show that DLEA has great advantages. |
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| AbstractList | For many-objective problems, how to maintain the diversity and convergence of the distribution of the solution set over Pareto front (PF) has always been the research emphasis. In the iteration process, the state of population is critical to improve the level of evolution. Therefore, this paper will use two convergence and diversity indicators to further strengthen the usage of evolutionary state information and propose a dynamic learning strategy. In addition, a dynamic learning strategy based many-objective evolutionary algorithm (MaOEA) is proposed, called dynamic learning evolution algorithm (DLEA), which continuously changes the direction of learning: convergence and diversity in the iteration process. The purpose is to make the algorithm prefer to convergence in the early iteration and prefer to diversity when it is close to PF in the late iteration, so that the convergence and diversity of the final solution set can be well maintained. And then, the performance of DLEA is measured by two indicators. Meanwhile, DLEA will be compared with four state-of-the-art algorithms on the DTLZ and MaF, and its performance will be verified on a many-objective combinatorial problem. And the experimental results and Friedman test show that DLEA has great advantages. |
| Author | Li, Gui Dong, Junyu Li, Keqin Wang, Gai-Ge Yeh, Wei-Chang |
| Author_xml | – sequence: 1 givenname: Gui surname: Li fullname: Li, Gui email: liguiatqingdao@gmail.com organization: Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China – sequence: 2 givenname: Gai-Ge orcidid: 0000-0002-3295-8972 surname: Wang fullname: Wang, Gai-Ge email: gaigewang@gmail.com, gaigewang@163.com organization: Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China – sequence: 3 givenname: Junyu surname: Dong fullname: Dong, Junyu email: dongjunyu@ouc.edu.cn organization: Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China – sequence: 4 givenname: Wei-Chang surname: Yeh fullname: Yeh, Wei-Chang email: yeh@ieee.org organization: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan – sequence: 5 givenname: Keqin surname: Li fullname: Li, Keqin email: lik@newpaltz.edu organization: Department of Computer Science, State University of New York, NY 12561, USA |
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| Keywords | Evolutionary algorithms (EAs) Many-objective optimization Performance indicators Dynamic learning strategy |
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