An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions
By Zhun Fan, Wenji Li, Xinye Cai, Han Huang, Yi Fang, Yugen You, Jiajie Mo, Caimin Wei, Erik D. Goodman. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2017
394 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/3a3fa7591fc4498c76bf63f54d48b7c2ed0def7e
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Citations in the corpus
- Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, background): “A typical CMOP of this type is the robot gripper optimization problem [12].”
- A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “…OSY test function [135], CTP test set [136], CF test set [137], NCTP test set [138], C-DTLZ test set [139], CMOP test set[140], LIR-CMOP test set [22], MW test set [18], DOC test set [96], DAS-CMOPs test set [23], CF test set [17], some test problems derived from actual problems [141], and…”
- A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, methodology): “This section verifies the performance of the proposed CCMO by comparing it to the constrained NSGA-II [13], PPS [15], C-TAEA [14], and ToP [10] on the constrained DTLZ test suite [14], [25], the MW test suite [6], the LIR-CMOP test suite [44], and the DOC test suite [10].”
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, methodology): “To relax the definition of feasibility, the algorithm in [24] regards solutions with small constraint violations as feasible ones, the algorithm in [12] regards solutions with constraint violations smaller than a predefined parameter ε as feasible ones, the algorithm in [25] makes no difference when…”