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

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)...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 23; číslo 23; s. 12491 - 12510
Hlavní autoři: Fan, Zhun, Li, Wenji, Cai, Xinye, Huang, Han, Fang, Yi, You, Yugen, Mo, Jiajie, Wei, Caimin, Goodman, Erik
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
Springer Nature B.V
Témata:
Artificial Intelligence
Collaboration
Computational Intelligence
Constraints
Control
Decomposition
Engineering
Evolutionary algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Methods
Multiple objective analysis
Optimization
Performance evaluation
Robotics
Violations
Constrained multi-objective optimization
Robot gripper optimization
Constrained multi-objective evolutionary algorithms
Epsilon constraint handling
ISSN:1432-7643, 1433-7479
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-03794-x