Journal
SOFT COMPUTING
Volume 23, Issue 23, Pages 12491-12510Publisher
SPRINGER
DOI: 10.1007/s00500-019-03794-x
Keywords
Constrained multi-objective evolutionary algorithms; Epsilon constraint handling; Constrained multi-objective optimization; Robot gripper optimization
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61300159, 75661473241, 61332002]
- Natural Science Foundation of Jiangsu Province of China [SBK2018022017]
- Hong Kong, Macao & Taiwan Science and Technology Cooperation Innovation Platform in Universities in Guangdong Province [2015KGJH2014]
- China Postdoctoral Science Foundation [2015M571751]
- Science and Technology Planning Project of Guangdong Province of China [2013B011304002]
- Educational Commission of Guangdong Province of China [2015KGJHZ014]
- Fundamental Research Funds for the Central Universities of China [NZ2013306]
- Guangdong High-Level University Project Green Technologies for Marine Industries
- Scientific Startup Research Foundation of Shantou University [NTF12024]
- State Key Lab of Digital Manufacturing Equipment Technology [DMETKF2019020]
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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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