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) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) 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 LIRCMOPs. Then the fourteen benchmarks, including LIRCMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/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. Department of Electronic Engineering, Shantou University, Guangdong, 515063, China College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 210016, China School of Software Engineering, South China University of Technology, Guangdong, 515063, China Department of Mathematics, Shantou University, Guangdong, 515063, China BEACON Center for the Study of Evolution in Action, Michigan State University. East Lansing, Michigan, USA. The experimental results demonstrate that MOEA/DIEpsilon also outperforms the other four CMOEAs on this problem.