Study on an Improved Extended PSO Algorithm Based on Geese Flight
LIU Hao-ran1,2,CUI Jing-chuang2,LU Ze-dan2,GUO Chang-jiang2,DING Pan2
1. Hebei Province Key Laboratory of Special Optical Fiber & Optical Fiber Sensing, Qinhuangdao, Hebei 066004, China
2. Information Science and Engineering College of Yanshan University, Qinhuangdao, Hebei 066004, China
3. Liren College of Yanshan University, Qinhuangdao, Hebei 066004, China
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.
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LIU Hao-ran,CUI Jing-chuang,LU Ze-dan,GUO Chang-jiang,DING Pan. Study on an Improved Extended PSO Algorithm Based on Geese Flight. Acta Metrologica Sinica, 2019, 40(3): 498-504.
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2019, Vol. 40 
