Difficulty Controllable and Scalable Constrained Multi-objective Test Problems
In this paper, we propose a general toolkit to construct constrained multi-objective optimisation problems (CMOPs) with three different kinds of constraint functions. Based on this toolkit, we suggested eight constrained multi-objective optimisation problems named CMOP1-CMOP8. As the ratio of feasib...
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| Veröffentlicht in: | 2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration S. 76 - 83 |
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01.12.2015
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| Abstract | In this paper, we propose a general toolkit to construct constrained multi-objective optimisation problems (CMOPs) with three different kinds of constraint functions. Based on this toolkit, we suggested eight constrained multi-objective optimisation problems named CMOP1-CMOP8. As the ratio of feasible regions in the whole search space determines the difficulty of a constrained multi-objective optimisation problem, we propose four test instances CMOP3-6, which have very low ratio of feasible regions. To study the difficulties of proposed test instances, we make some experiments with two popular CMOEAs - MOEA/D-CDP and NSGA-II-CDP, and analysed their performances. |
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| AbstractList | In this paper, we propose a general toolkit to construct constrained multi-objective optimisation problems (CMOPs) with three different kinds of constraint functions. Based on this toolkit, we suggested eight constrained multi-objective optimisation problems named CMOP1-CMOP8. As the ratio of feasible regions in the whole search space determines the difficulty of a constrained multi-objective optimisation problem, we propose four test instances CMOP3-6, which have very low ratio of feasible regions. To study the difficulties of proposed test instances, we make some experiments with two popular CMOEAs - MOEA/D-CDP and NSGA-II-CDP, and analysed their performances. |
| Author | Wenji Li Haibin Yin Zhun Fan Xinye Cai Hui Li Kaiwen Hu |
| Author_xml | – sequence: 1 surname: Zhun Fan fullname: Zhun Fan organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China – sequence: 2 surname: Wenji Li fullname: Wenji Li organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China – sequence: 3 surname: Xinye Cai fullname: Xinye Cai organization: Coll. of Comput. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China – sequence: 4 surname: Hui Li fullname: Hui Li organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China – sequence: 5 surname: Kaiwen Hu fullname: Kaiwen Hu organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China – sequence: 6 surname: Haibin Yin fullname: Haibin Yin organization: Sch. of Mech. & Electron. Eng., Wuhan Univ. of Technol. Wuhan, Wuhan, China |
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| Snippet | In this paper, we propose a general toolkit to construct constrained multi-objective optimisation problems (CMOPs) with three different kinds of constraint... |
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| SubjectTerms | Aerospace electronics Computer science Constrained Multi-objective Evolutionary Algorithm Constrained Multi-objective Optimisation problem Evolutionary computation Linear programming Pareto optimization Shape |
| Title | Difficulty Controllable and Scalable Constrained Multi-objective Test Problems |
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