An evolutionary algorithm with clustering-based selection strategies for multi-objective optimization
This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to approp...
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| Vydané v: | Information sciences Ročník 624; s. 217 - 234 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.05.2023
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to appropriately search the space. The local indicator selection is developed to select diverse and well-converged individuals for mating while the local crowding selection strategy is designed to maintain a set of evenly distributed individuals on the Pareto front for next generation of evolution. The proposed method is further enhanced by a clustering based crowding degree strategy, which is introduced to extract a uniformly distributed and convergent solutions as the final output. The performance of proposed algorithm has been evaluated on 31 benchmark problems and compared with related methods. The results clearly show the merits of proposed strategies and the proposed method could significantly outperform related methods to be compared. |
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| AbstractList | This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to appropriately search the space. The local indicator selection is developed to select diverse and well-converged individuals for mating while the local crowding selection strategy is designed to maintain a set of evenly distributed individuals on the Pareto front for next generation of evolution. The proposed method is further enhanced by a clustering based crowding degree strategy, which is introduced to extract a uniformly distributed and convergent solutions as the final output. The performance of proposed algorithm has been evaluated on 31 benchmark problems and compared with related methods. The results clearly show the merits of proposed strategies and the proposed method could significantly outperform related methods to be compared. |
| Author | Chen, Tianxiang Zheng, Yujun Li, Qi Mo, Xiaomei Sheng, Weiguo Zhou, Shenghao Wang, Zidong |
| Author_xml | – sequence: 1 givenname: Shenghao surname: Zhou fullname: Zhou, Shenghao organization: Department of Computer Science, Hangzhou Normal University, Hangzhou 311121, PR China – sequence: 2 givenname: Xiaomei surname: Mo fullname: Mo, Xiaomei organization: College of Media Engineering, Communication University of Zhejiang, Hangzhou 310018, PR China – sequence: 3 givenname: Zidong surname: Wang fullname: Wang, Zidong organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK – sequence: 4 givenname: Qi surname: Li fullname: Li, Qi organization: Department of Computer Science, Hangzhou Normal University, Hangzhou 311121, PR China – sequence: 5 givenname: Tianxiang surname: Chen fullname: Chen, Tianxiang organization: Department of Computer Science, San Jose State university, 1 Washington SQ, San Jose, CA 95192, USA – sequence: 6 givenname: Yujun surname: Zheng fullname: Zheng, Yujun organization: Department of Computer Science, Hangzhou Normal University, Hangzhou 311121, PR China – sequence: 7 givenname: Weiguo surname: Sheng fullname: Sheng, Weiguo email: w.sheng@ieee.org organization: Department of Computer Science, Hangzhou Normal University, Hangzhou 311121, PR China |
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| Keywords | I∊+ indicator Multi-objective evolution algorithm Clustering Selection strategy |
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