Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (1): 245-250.doi: 10.11896／j.issn.1002-137X.2019.01.038

• Artificial Intelligence • Previous Articles     Next Articles

S-shaped Function Based Adaptive Particle Swarm Optimization Algorithm

HUANG Yang1, LU Hai-yan1,2, XU Kai-bo1, HU Shi-juan1   

  1. (School of Science,Jiangnan University,Wuxi,Jiangsu 214122,China)1
    (Wuxi Engineering Technology Research Center for Biological Computing,Wuxi,Jiangsu 214122,China)2
  • Received:2017-11-24 Online:2019-01-15 Published:2019-02-25

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Abstract: Aiming at the problems of low solution precision and slow convergence speed in the later stage of particle swarm optimization algorithm,this paper presented an S-shaped function based adaptive particle swarm optimization algorithm (SAPSO).This algorithm takes advantage of the characteristics of upside-down S-shaped function to adjust the inertia weight nonlinearly,better balancing the global search ability and local search ability.In addition,an S-shape function is introduced into the position updating equation,and the ratio of the individual particle’s fitness value to the swarm’s average fitness value is used to adaptively adjust the step size in the search,thus enhancing the efficiency of the algorithm.Simulation results on a set of typical test functions show that SAPSO is superior to several existing improved PSO algorithms significantly in terms of the convergence rate and solution accuracy.

Key words: Inertia weight, Particle swarm optimization algorithm, Position updating, S-shaped function

CLC Number: 

  • TP301
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