A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems
Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set...
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| Vydané v: | IEEE transactions on evolutionary computation Ročník 22; číslo 1; s. 47 - 60 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
01.02.2018
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set-based genetic algorithm to effectively solve them. The original optimization problem was first transformed into a deterministic bi-objective problem, where new objectives are hyper-volume and imprecision. A set-based Pareto dominance relation was then defined to modify the fast nondominated sorting approach in NSGA-II. Additionally, set-based evolutionary schemes were suggested. Finally, our method was empirically evaluated on 39 benchmark IMaOPs as well as a car cab design problem and compared with two typical methods. The numerical results demonstrated the superiority of our method and indicated that a tradeoff approximate front between convergence and uncertainty can be produced. |
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| AbstractList | Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set-based genetic algorithm to effectively solve them. The original optimization problem was first transformed into a deterministic bi-objective problem, where new objectives are hyper-volume and imprecision. A set-based Pareto dominance relation was then defined to modify the fast nondominated sorting approach in NSGA-II. Additionally, set-based evolutionary schemes were suggested. Finally, our method was empirically evaluated on 39 benchmark IMaOPs as well as a car cab design problem and compared with two typical methods. The numerical results demonstrated the superiority of our method and indicated that a tradeoff approximate front between convergence and uncertainty can be produced. |
| Author | Gong, Dunwei Sun, Jing Miao, Zhuang |
| Author_xml | – sequence: 1 givenname: Dunwei surname: Gong fullname: Gong, Dunwei email: dwgong@vip.163.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221008, China – sequence: 2 givenname: Jing orcidid: 0000-0002-1485-0247 surname: Sun fullname: Sun, Jing email: sunj@hhit.edu.cn organization: School of Sciences, Huaihai Institute of Technology, Lianyungang, China – sequence: 3 givenname: Zhuang surname: Miao fullname: Miao, Zhuang email: 1037407168@qq.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221008, China |
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| SubjectTerms | Context Convergence Genetic algorithm Genetic algorithms interval many-objective optimization Noise measurement Optimization set-based evolution Sorting Uncertainty |
| Title | A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems |
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