An improved epsilon constraint handling method embedded in MOEA/D for constrained multi-objective optimization problems

This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critic...

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Published in2016 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 1 - 8
Main Authors Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Han Huang, Zhaoquan Cai, Caimin Wei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Evolutionary computation
Maintenance engineering
Optimization
Radiation detectors
Radio frequency
Sociology
Statistics
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DOI10.1109/SSCI.2016.7850224

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Abstract This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critical parameter in the epsilon constraint method, according to the feasible ratio of solutions in the current population. In order to verify the effect of the improved epsilon constraint handling method, three algorithms - MOEA/D-CDP, MOEA/D-Epsilon, and MOEA/D-IEpsilon (MOEA/D with the improved epsilon constraint handling mechanism) are tested on nine CMOPs (CMOP1-CMOP9). The comprehensive experimental results indicate that the proposed epsilon constraint handling method is very effective on the performance of both convergence and diversity.
AbstractList This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critical parameter in the epsilon constraint method, according to the feasible ratio of solutions in the current population. In order to verify the effect of the improved epsilon constraint handling method, three algorithms - MOEA/D-CDP, MOEA/D-Epsilon, and MOEA/D-IEpsilon (MOEA/D with the improved epsilon constraint handling mechanism) are tested on nine CMOPs (CMOP1-CMOP9). The comprehensive experimental results indicate that the proposed epsilon constraint handling method is very effective on the performance of both convergence and diversity.
Author Han Huang
Zhaoquan Cai
Caimin Wei
Wenji Li
Zhun Fan
Xinye Cai
Hui Li
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  organization: Dept. of Electron. Eng., Shantou Univ., Shantou, China
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  organization: Dept. of Comput. Sci., Huizhou Univ., Huizhou, China
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  surname: Caimin Wei
  fullname: Caimin Wei
  organization: Dept. of Math., Shantou Univ., Shantou, China
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Snippet This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to...
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SubjectTerms Evolutionary computation
Maintenance engineering
Optimization
Radiation detectors
Radio frequency
Sociology
Statistics
Title An improved epsilon constraint handling method embedded in MOEA/D for constrained multi-objective optimization problems
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