基于速度交流的多种群多目标粒子群算法研究

doi:10.3969/j.issn.1000-1158.2020.08.18

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Abstract In order to improve the convergence precision and search performance of multi-objective optimization algorithms, a multi-population of multi-objective particle swarm optimization algorithm based on velocity communication is proposed. The algorithm introduces the speed communication mechanism, divides the population into multiple sub-populations to achieve speed information sharing, improves the particle single search mode, and enhances the global search ability of the algorithm. Chaos mapping is used to optimize the inertia weight, and the particle search ergodicity and globality are improved. In order to reduce the possibility that the algorithm falls into the local optimal Pareto frontier in the late stage of operation, different mutation operations are performed on each sub-population. The algorithm is compared with NSGA-Ⅱ, SPEA2, AbYSS, MOPSO, SMPSO and GWASF-GA state-of-the-art multi-objective optimization algorithms. Experimental results show that the solution set obtained by this algorithm has better convergence and distribution.

Key words： metrology multi-objective particle swarm optimization multi-population speed communication

Received: 22 January 2019 Published: 13 August 2020

PACS:	TB973
	TB934

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	Articles by authors
	LIU Bin
	LIU Ze-ren
	ZHAO Zhi-biao
	LI Rui
	WEN Yan
	LIU Hao-ran

Cite this article:

LIU Bin,LIU Ze-ren,ZHAO Zhi-biao, et al. Research on Multi-population Multi-objective Particle Swarm Optimization Algorithm Based on Velocity Communication[J]. Acta Metrologica Sinica, 2020, 41(8): 1002-1011.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.08.18 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I8/1002

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