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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Published in:2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration pp. 76 - 83
Main Authors: Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Kaiwen Hu, Haibin Yin
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2015
Subjects:
Aerospace electronics
Computer science
Constrained Multi-objective Evolutionary Algorithm
Constrained Multi-objective Optimisation problem
Evolutionary computation
Linear programming
Pareto optimization
Shape
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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.
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
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  organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China
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  organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China
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  surname: Hui Li
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  organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China
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  surname: Kaiwen Hu
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  organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China
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  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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