Multimodal multi-objective evolutionary algorithm for multiple path planning
•The differences between discrete and continuous MOPs are analyzed.•A special environmental selection strategy is proposed to maintain the diversity.•A diversity-based fitness indicator is proposed.•A novel multimodal multi-objective evolutionary algorithm is proposed. The multi-objective path plann...
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| Vydáno v: | Computers & industrial engineering Ročník 169; s. 108145 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2022
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | •The differences between discrete and continuous MOPs are analyzed.•A special environmental selection strategy is proposed to maintain the diversity.•A diversity-based fitness indicator is proposed.•A novel multimodal multi-objective evolutionary algorithm is proposed.
The multi-objective path planning problem has received much attention recently. Traditional solving methods try to find a single optimal path without considering the multiformity of the paths. In this study, we first analyze the situation that several different paths may have the same objective values, termed as multi-modal minimum path problems. To address these problems, we propose a novel solution-encoding method, which decreases the size of decision-space greatly. Then, to maintain the population diversity in the decision space, we propose an environmental selection strategy, in which the duplicate solutions are deleted first and then a second-selection method is adopted. Finally, an effective multi-objective evolutionary algorithm based on the special environmental selection is proposed, termed MMEA-SES. Through the experiments, the proposed method is proved effective and efficient compared to other state-of-the-art algorithms for multimodal multi-objective path planning. |
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| AbstractList | •The differences between discrete and continuous MOPs are analyzed.•A special environmental selection strategy is proposed to maintain the diversity.•A diversity-based fitness indicator is proposed.•A novel multimodal multi-objective evolutionary algorithm is proposed.
The multi-objective path planning problem has received much attention recently. Traditional solving methods try to find a single optimal path without considering the multiformity of the paths. In this study, we first analyze the situation that several different paths may have the same objective values, termed as multi-modal minimum path problems. To address these problems, we propose a novel solution-encoding method, which decreases the size of decision-space greatly. Then, to maintain the population diversity in the decision space, we propose an environmental selection strategy, in which the duplicate solutions are deleted first and then a second-selection method is adopted. Finally, an effective multi-objective evolutionary algorithm based on the special environmental selection is proposed, termed MMEA-SES. Through the experiments, the proposed method is proved effective and efficient compared to other state-of-the-art algorithms for multimodal multi-objective path planning. |
| ArticleNumber | 108145 |
| Author | Yao, Xingyi Li, Wenhua Pan, Xiaogang Wang, Rui |
| Author_xml | – sequence: 1 givenname: Xingyi surname: Yao fullname: Yao, Xingyi organization: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China – sequence: 2 givenname: Wenhua surname: Li fullname: Li, Wenhua organization: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China – sequence: 3 givenname: Xiaogang surname: Pan fullname: Pan, Xiaogang organization: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China – sequence: 4 givenname: Rui surname: Wang fullname: Wang, Rui email: ruiwangnudt@gmail.com organization: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China |
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| Cites_doi | 10.1016/j.swevo.2019.100570 10.1109/TEVC.2016.2611642 10.2514/1.46478 10.1109/4235.996017 10.1016/j.ins.2020.03.007 10.1109/TEVC.2017.2776226 10.1016/j.ejor.2006.06.042 10.1109/TEVC.2021.3078441 10.1016/j.swevo.2018.10.016 10.1016/j.swevo.2019.06.001 10.1109/TEVC.2017.2754271 10.1109/ACCESS.2018.2832181 10.1109/TEVC.2003.810758 |
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| Keywords | Multiple Path planning Discrete optimization Multi-objective optimization Multi-modal Evolutionary algorithm |
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| SubjectTerms | Discrete optimization Evolutionary algorithm Multi-modal Multi-objective optimization Multiple Path planning |
| Title | Multimodal multi-objective evolutionary algorithm for multiple path planning |
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