A niching indicator-based multi-modal many-objective optimizer
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective...
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| Veröffentlicht in: | Swarm and evolutionary computation Jg. 49; S. 134 - 146 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.09.2019
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| ISSN: | 2210-6502 |
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| Abstract | Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms. |
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| AbstractList | Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms. |
| Author | Tanabe, Ryoji Ishibuchi, Hisao |
| Author_xml | – sequence: 1 givenname: Ryoji surname: Tanabe fullname: Tanabe, Ryoji email: rt.ryoji.tanabe@gmail.com – sequence: 2 givenname: Hisao surname: Ishibuchi fullname: Ishibuchi, Hisao email: hisao@sustech.edu.cn |
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| Keywords | Multi-modal multi-objective optimization Indicator-based evolutionary algorithms Niching methods Many-objective optimization |
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| Snippet | Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for... |
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| StartPage | 134 |
| SubjectTerms | Indicator-based evolutionary algorithms Many-objective optimization Multi-modal multi-objective optimization Niching methods |
| Title | A niching indicator-based multi-modal many-objective optimizer |
| URI | https://dx.doi.org/10.1016/j.swevo.2019.06.001 |
| Volume | 49 |
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