A decomposition-based multi-objective evolutionary algorithm with quality indicator
The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems...
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| Veröffentlicht in: | Swarm and evolutionary computation Jg. 39; S. 339 - 355 |
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01.04.2018
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| Abstract | The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail. |
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| AbstractList | The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail. |
| Author | Liu, Qiqi Chen, Minrong Gao, Kaizhou Luo, Jianping Yang, Yun Li, Xia |
| Author_xml | – sequence: 1 givenname: Jianping surname: Luo fullname: Luo, Jianping email: ljp@szu.edu.cn organization: College of Information Engineering, Shenzhen University, Shenzhen 518060, PR China – sequence: 2 givenname: Yun surname: Yang fullname: Yang, Yun organization: College of Information Engineering, Shenzhen University, Shenzhen 518060, PR China – sequence: 3 givenname: Xia surname: Li fullname: Li, Xia organization: College of Information Engineering, Shenzhen University, Shenzhen 518060, PR China – sequence: 4 givenname: Qiqi surname: Liu fullname: Liu, Qiqi organization: College of Information Engineering, Shenzhen University, Shenzhen 518060, PR China – sequence: 5 givenname: Minrong surname: Chen fullname: Chen, Minrong organization: South China Normal University, Guangzhou 510631, PR China – sequence: 6 givenname: Kaizhou surname: Gao fullname: Gao, Kaizhou organization: School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore |
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