Multi-Objective Optimization Based on Brain Storm Optimization Algorithm
In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objectiv...
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| Veröffentlicht in: | International journal of swarm intelligence research Jg. 4; H. 3; S. 1 - 21 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IGI Global
01.07.2013
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| ISSN: | 1947-9263, 1947-9271 |
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| Abstract | In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems. |
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| AbstractList | In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems. |
| Author | Xue, Jingqian Wu, Yali Shi, Yuhui |
| AuthorAffiliation | Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China Xi’an University of Technology, Xi’an, China Huawei, Xi’an, China |
| AuthorAffiliation_xml | – name: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China – name: Huawei, Xi’an, China – name: Xi’an University of Technology, Xi’an, China |
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| Title | Multi-Objective Optimization Based on Brain Storm Optimization Algorithm |
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