MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

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 algorith...

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Bibliographic Details
Published in:	Applied soft computing Vol. 74; pp. 621 - 633
Main Authors:	Fan, Zhun, Fang, Yi, Li, Wenji, Cai, Xinye, Wei, Caimin, Goodman, Erik
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.01.2019
Subjects:	Angle-based constrained dominance principle (ACDP) Constrained multi-objective evolutionary algorithms (CMOEAs) Constraint-handling mechanism Constraint-handling mechanism Constrained multi-objective evolutionary algorithms (CMOEAs) Angle-based constrained dominance principle (ACDP)
ISSN:	1568-4946, 1872-9681
Online Access:	Get full text
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Summary:	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, NSGA-II-CDP and SP, are compared. The experimental results illustrate that MOEA/D-ACDP is significantly better than the other six CMOEAs on these benchmark problems and the real-world case, which demonstrates the effectiveness of ACDP. •The proposed MOEA/D-ACDP utilizes the angle information to maintain the diversity.•MOEA/D-ACDP enhances convergence to the PF by exploring infeasible regions.•MOEA/D-ACDP is significantly better than the other six CMOEAs on the benchmark problems.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2018.10.027