D~2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces

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

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D~2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces

机译：D~2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces

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摘要

This paper improves a recently developed multi-objective particle swarm optimizer (D~2MOPSO) 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. D~2MOPSO introduces a new archiving technique that facilitates attaining better diversity and coverage in both objective and solution spaces. The improvedmethod 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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《Evolutionary computation》 |2014年第1期|47-77|共31页
作者
N. Al Moubayed; A. Petrovski; J. McCall;
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作者单位

Robert Gordon University, Aberdeen, AB25 1HG, UK;

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正文语种英语
中图分类计算技术、计算机技术;
关键词
Multi-objective particle swarm optimization; decomposition-based evolutionary algorithms; dominance; archiving technique; OMOPSO; MOEA/D;

D~2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces

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