Many-objective evolutionary algorithm based on relative non-dominance matrix
Various evolutionary algorithms have been proposed for tackling many-objective optimization problems over the past three decades. However, these algorithms still suffer from the loss of selection pressures due to the existence of dominance resistance. To tackle this issue, this paper proposes a rela...
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| Published in | Information sciences Vol. 547; pp. 963 - 983 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
08.02.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2020.09.061 |
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| Abstract | Various evolutionary algorithms have been proposed for tackling many-objective optimization problems over the past three decades. However, these algorithms still suffer from the loss of selection pressures due to the existence of dominance resistance. To tackle this issue, this paper proposes a relative non-dominance matrix, based on which a fitness formula is defined. Empirical analyses show that solutions with smaller fitness values are likely to dominate more other solutions in the future evolutionary process, and play a vital role in enhancing the convergence toward to the true Pareto fronts. Additionally, to further ensure the diversity, k-means clustering strategy is combined with the relative non-dominance matrix for a new design of the environmental selection, where parameter k in the clustering strategy is adjusted adaptively. The proposed algorithm is extensively tested with four state-of-art algorithms on WFG, MaF and DTLZ test suites. Empirical comparisons demonstrate the competitiveness of the proposed algorithm regarding to the convergence, diversity and spread. |
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| AbstractList | Various evolutionary algorithms have been proposed for tackling many-objective optimization problems over the past three decades. However, these algorithms still suffer from the loss of selection pressures due to the existence of dominance resistance. To tackle this issue, this paper proposes a relative non-dominance matrix, based on which a fitness formula is defined. Empirical analyses show that solutions with smaller fitness values are likely to dominate more other solutions in the future evolutionary process, and play a vital role in enhancing the convergence toward to the true Pareto fronts. Additionally, to further ensure the diversity, k-means clustering strategy is combined with the relative non-dominance matrix for a new design of the environmental selection, where parameter k in the clustering strategy is adjusted adaptively. The proposed algorithm is extensively tested with four state-of-art algorithms on WFG, MaF and DTLZ test suites. Empirical comparisons demonstrate the competitiveness of the proposed algorithm regarding to the convergence, diversity and spread. |
| Author | Li, Wuzhao Li, Dongyang Wu, Qidi Zhang, Maoqing Wang, Lei Hu, Bo Guo, Weian |
| Author_xml | – sequence: 1 givenname: Maoqing orcidid: 0000-0003-1362-4384 surname: Zhang fullname: Zhang, Maoqing organization: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China – sequence: 2 givenname: Lei orcidid: 0000-0002-2146-9081 surname: Wang fullname: Wang, Lei email: wanglei@tongji.edu.cn organization: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China – sequence: 3 givenname: Weian orcidid: 0000-0001-9715-3836 surname: Guo fullname: Guo, Weian organization: Sino-Germany College of Applied Sciences, Tongji University, Shanghai 201804, China – sequence: 4 givenname: Wuzhao surname: Li fullname: Li, Wuzhao organization: School of Intelligent Manufacturing, Jiaxing Vocational Technology College, 314036 Zhejiang, China – sequence: 5 givenname: Dongyang surname: Li fullname: Li, Dongyang organization: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China – sequence: 6 givenname: Bo surname: Hu fullname: Hu, Bo organization: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China – sequence: 7 givenname: Qidi surname: Wu fullname: Wu, Qidi organization: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China |
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