一种改进的雁群扩展粒子群算法研究

doi:10.3969/j.issn.1000-1158.2019.03.24

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Abstract To solve the local minimum problem of PSO, a new algorithm called GeEPSO is proposed, which hybrids the PSO based on the flight of geese and the extended PSO algorithm. The GeEPSO take the advantage of the various directions of geese and make full use of the position of each particle to assure the diversity of particle swarm. And to further improve the convergence performance, the simplified PSO is introduced to obtain another new algorithm GeESPSO. The simulation results of some benchmarks demonstrate the GeEPSO algorithm can deal well with the conflict between the convergence rate and the local minimum problem, which is better on the whole. And to further investigate the effect of the proposed algorithms in practical application, the new algorithms were used to optimize the BP neural network. Finally the prediction of PM2.5 is made with the improved BP neural network, using the meteorological data.

Key words： metrology particle swarm optimization flight of geese BP neural network PM2.5 prediction

Received: 03 April 2018 Published: 19 April 2019

PACS:

TB973

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	Articles by authors
	LIU Hao-ran
	CUI Jing-chuang
	LU Ze-dan
	GUO Chang-jiang
	DING Pan

Cite this article:

LIU Hao-ran,CUI Jing-chuang,LU Ze-dan, et al. Study on an Improved Extended PSO Algorithm Based on Geese Flight[J]. Acta Metrologica Sinica, 2019, 40(3): 498-504.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.03.24 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I3/498

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