Handling multi-objective optimization problems with unbalanced constraints and their effects on evolutionary algorithm performance

Despite the successful application of an extension of the Multi-Objective Evolution Algorithm based on Decomposition (MOEA/D-M2M) to solve unbalanced multi-objective optimization problems (UMOPs), its use in constrained unbalanced multi-objective optimization problems has not been fully explored. In...

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Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 55; p. 100676
Main Authors: Peng, Chaoda, Liu, Hai-Lin, Goodman, Erik D.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2020
Subjects:
Constraint-handling technique
Evolutionary algorithm
MOEA/D-M2M
Multi-objective
Unbalanced constraints
Multi-objective
Constraint-handling technique
Unbalanced constraints
MOEA/D-M2M
Evolutionary algorithm
ISSN:2210-6502
Online Access:Get full text
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Summary:Despite the successful application of an extension of the Multi-Objective Evolution Algorithm based on Decomposition (MOEA/D-M2M) to solve unbalanced multi-objective optimization problems (UMOPs), its use in constrained unbalanced multi-objective optimization problems has not been fully explored. In an earlier paper, a definition of UMOPs was suggested that had two necessary conditions: 1) finding a favored subset of the Pareto set is easier than finding an unfavored subset, and 2) the favored subset of the Pareto set dominates a large part of the feasible space. The second condition strongly reduces the fraction of MOPs that are considered UMOPs. In this paper, we eliminate that second condition and consider a broader class of UMOPs. We design an unbalanced constrained multi-objective test suite with three different types of biased constraints, yielding three different types of constrained test problems in which the degree of imbalance is scalable via a set of parameters introduced for each problem. We analyse the characteristics of three types of constraints and the difficulties they present for potential solution algorithms–i.e., NSGA-II, MOEA/D and MOEA/D-M2M, with four constraint-handling techniques. MOEA/D-M2M is shown to significantly outperform the other algorithms on these problems due to its decomposition strategy.
ISSN:2210-6502
DOI:10.1016/j.swevo.2020.100676