Name
Title | ||
|---|---|---|
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00500-019-03794-x | Soft Computing |
Keywords | Field | DocType |
Constrained multi-objective evolutionary algorithms, Epsilon constraint handling, Constrained multi-objective optimization, Robot gripper optimization | Population,Mathematical optimization,Evolutionary algorithm,Computer science,Robot,Optimization problem | Journal |
Volume | Issue | ISSN |
abs/1707.08767 | 23.0 | 1432-7643 |
Citations | PageRank | References |
11 | 0.48 | 25 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhun Fan | 1 | 106 | 13.81 |
| Wenji Li | 2 | 65 | 8.74 |
| Xinye Cai | 3 | 63 | 5.14 |
| Han Huang | 4 | 22 | 3.38 |
| Yi Fang | 5 | 27 | 1.75 |
| Yugen You | 6 | 17 | 2.24 |
| Jiajie Mo | 7 | 11 | 0.48 |
| Caimin Wei | 8 | 47 | 2.42 |
| Erik Goodman | 9 | 145 | 15.19 |