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Soft Computing

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04.02.2019 | Methodologies and Application

An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

verfasst von: Zhun Fan, Wenji Li, Xinye Cai, Han Huang, Yi Fang, Yugen You, Jiajie Mo, Caimin Wei, Erik Goodman

Erschienen in: Soft Computing | Ausgabe 23/2019

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Abstract

This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem.

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Titel: An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions
verfasst von: Zhun Fan
Wenji Li
Xinye Cai
Han Huang
Yi Fang
Yugen You
Jiajie Mo
Caimin Wei
Erik Goodman
Publikationsdatum: 04.02.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 23/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-03794-x

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