.png){kind=icon}
.png){kind=icon}
Return
MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2018.10.027.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a novel constraint-handling mechanism, namely the angle-based constrained dominance principle (ACDP), to solve constrained multi-objective optimization problems (CMOPs). In this work, the mechanism of ACDP is embedded in a decomposition-based multi-objective evolutionary algorithm (MOEA/D). ACDP uses the angle information among solutions of a population and the proportion of feasible solutions to adjust the dominance relationship, so that it can maintain good convergence, diversity and feasibility of a population, simultaneously. To evaluate the performance of the proposed MOEA/D-ACDP, fourteen benchmark instances and an engineering optimization problem are studied. Six state-of-the-art CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon, MOEA/D-SR, NSGAII-CDP and SP, are compared. The experimental results illustrate that MOEA/D-ACDP is significantly better than the other six CM0EAs on these benchmark problems and the real-world case, which demonstrates the effectiveness of ACDP. (C) 2018 Published by Elsevier B.V.
Keywords:
Constraint-handling mechanism
Angle-based constrained dominance principle (ACDP)
Constrained multi-objective evolutionary algorithms (CMOEAs) Applied
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.