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

被引:178
作者
Al Moubayed, N. [1 ]
Petrovski, A. [1 ]
McCall, J. [1 ]
机构
[1] Robert Gordon Univ, Aberdeen AB25 1HG, Scotland
关键词
Multi-objective particle swarm optimization; decomposition-based evolutionary algorithms; dominance; archiving technique; OMOPSO; MOEA; D; MULTIOBJECTIVE OPTIMIZATION; EVOLUTIONARY ALGORITHMS;
D O I
10.1162/EVCO_a_00104
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
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.
引用
收藏
页码:47 / 77
页数:31
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