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D2MOPSO : MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution spaces.

Al Moubayed, N and Petrovski, A and McCall, J (2014) 'D2MOPSO : MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution spaces.', Evolutionary domputation., 22 (1). pp. 47-77.

Abstract

This paper improves a recently developed multi-objective particle swarm optimizer () that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by transforming it to a set of aggregation problems, whereas dominance plays a major role in building the leaders’ archive. introduces a new archiving technique that facilitates attaining better diversity and coverage in both objective and solution spaces. The improved method is evaluated on standard benchmarks including both constrained and unconstrained test problems, by comparing it with three state of the art multi-objective evolutionary algorithms: MOEA/D, OMOPSO, and dMOPSO. The comparison and analysis of the experimental results, supported by statistical tests, indicate that the proposed algorithm is highly competitive, efficient, and applicable to a wide range of multi-objective optimization problems.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1162/EVCO_a_00104
Publisher statement:© 2014 by the Massachusetts Institute of Technology.
Date accepted:17 January 2014
Date deposited:23 March 2018
Date of first online publication:07 February 2014
Date first made open access:No date available

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