A decomposition-based evolutionary algorithm using an estimation strategy for multimodal multi-objective optimization
Multimodal multi-objective optimization problems (MMOPs) are commonly seen in real-world applications and have attracted a growing attention in recent years. In this paper, a decomposition-based evolutionary algorithm using an estimation strategy is presented to handle MMOPs. In the proposed algorit...
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01.08.2022
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| Abstract | Multimodal multi-objective optimization problems (MMOPs) are commonly seen in real-world applications and have attracted a growing attention in recent years. In this paper, a decomposition-based evolutionary algorithm using an estimation strategy is presented to handle MMOPs. In the proposed algorithm, multiple individuals who are assigned to the same weight vector form a subpopulation. Then, the estimation strategy is designed for estimating the amount of Pareto optimal sets (PSs), where the mean-shift algorithm is adopted to classify subpopulation into some clusters. In essence, the number of clusters is considered to be the estimated number of PSs. Finally, an environmental selection method, which combines the estimation strategy and the greedy selection, is adopted to dynamically adjust the subpopulation scale for maintaining the population diversity. The experimental results illustrate that the devised algorithm performs pass beyond the selected up-to-date competing algorithms. |
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| AbstractList | Multimodal multi-objective optimization problems (MMOPs) are commonly seen in real-world applications and have attracted a growing attention in recent years. In this paper, a decomposition-based evolutionary algorithm using an estimation strategy is presented to handle MMOPs. In the proposed algorithm, multiple individuals who are assigned to the same weight vector form a subpopulation. Then, the estimation strategy is designed for estimating the amount of Pareto optimal sets (PSs), where the mean-shift algorithm is adopted to classify subpopulation into some clusters. In essence, the number of clusters is considered to be the estimated number of PSs. Finally, an environmental selection method, which combines the estimation strategy and the greedy selection, is adopted to dynamically adjust the subpopulation scale for maintaining the population diversity. The experimental results illustrate that the devised algorithm performs pass beyond the selected up-to-date competing algorithms. |
| Author | Yen, Gary G. Gong, Maoguo Xu, Wei Gao, Weifeng |
| Author_xml | – sequence: 1 givenname: Weifeng surname: Gao fullname: Gao, Weifeng organization: School of Mathematics and Statistics, Xidian University, Xi’an 710126, China – sequence: 2 givenname: Wei surname: Xu fullname: Xu, Wei organization: School of Mathematics and Statistics, Xidian University, Xi’an 710126, China – sequence: 3 givenname: Maoguo surname: Gong fullname: Gong, Maoguo organization: School of Electronic Engineering, Xidian University, Xi’an 710126, China – sequence: 4 givenname: Gary G. orcidid: 0000-0001-8851-5348 surname: Yen fullname: Yen, Gary G. email: gyen@okstate.edu organization: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA |
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