Journal
EVOLUTIONARY COMPUTATION
Volume 22, Issue 1, Pages 47-77Publisher
MIT PRESS
DOI: 10.1162/EVCO_a_00104
Keywords
Multi-objective particle swarm optimization; decomposition-based evolutionary algorithms; dominance; archiving technique; OMOPSO; MOEA; D
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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.
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