D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces

Al Moubayed, N; Petrovski, A; McCall, J

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.

Citation

Al Moubayed, N., Petrovski, A., & McCall, J. (2014). D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces. Evolutionary Computation, 22(1), 47-77. https://doi.org/10.1162/evco_a_00104

• Journal Article Type: Article
• Acceptance Date: Jan 17, 2014
• Online Publication Date: Feb 7, 2014
• Publication Date: Feb 7, 2014
• Deposit Date: Jan 26, 2016
• Publicly Available Date: Mar 23, 2018
• Journal: Evolutionary Computation
• Print ISSN: 1063-6560
• Electronic ISSN: 1530-9304
• Publisher: Massachusetts Institute of Technology Press
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Issue: 1
• Pages: 47-77
• DOI: https://doi.org/10.1162/evco_a_00104
• Public URL: https://durham-repository.worktribe.com/output/1394019