基于速度交流的共生多种群粒子群算法

doi:10.3969/j.issn.1000-1158.2020.08.19

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Abstract In order to improve the accuracy and the search performance of the particle swarm optimization algorithm, symbiotic multi-population particle swarm optimization algorithm based on velocity communication(SMPSO) was proposed.The whole slave population was divided into multiple sub-populations by using the speed communication mechanism for the global search of the solution space, and the optimal state of the slave population was sent to the master population.The master population comprehensive the slave population experience and its own experience for the local deep optimization, and the best information was sent to the slave population, establishing symbiotic relationship between the master-slave population and achieving full search of the solution space.In the latter part of the iteration, the master population was combined with the adaptive mutation strategy, increasing the ability of the algorithm to jump out of the local optimum.The proposed SMPSO algorithm was applied to the benchmark function and compared with other improved PSO algorithms.The experimental results showed that the SMPSO algorithm has great improvement in solving accuracy and search ability.

Key words： metrology particle swarm optimization algorithm speed communication mechanism symbiotic adaptive mutation strategy

Received: 25 July 2018 Published: 13 August 2020

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TB973

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	ZHAO Zhi-biao
	LI Rui
	LIU Bin
	ZHOU Wu-zhou

Cite this article:

ZHAO Zhi-biao,LI Rui,LIU Bin, et al. Symbiosis Multi-population Particle Swarm Optimization Algorithm Based on Velocity Communication[J]. Acta Metrologica Sinica, 2020, 41(8): 1012-1022.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.08.19 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I8/1012

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