Computer Science ›› 2018, Vol. 45 ›› Issue (2): 98-102.doi: 10.11896/j.issn.1002-137X.2018.02.017

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Particle Swarm Optimization Algorithm with Dynamically Adjusting Inertia Weight

DONG Hong-bin, LI Dong-jin and ZHANG Xiao-ping   

  • Online:2018-02-15 Published:2018-11-13

Abstract: In order to tackle the problems of slow convergence,low accuracy and parameter dependence of the standard particle swarm optimization(PSO) algorithm,a nonlinear exponential inertia weight in particle swarm optimization(EIW-PSO) was proposed.In each iteration,the new algorithm improves its performance by adjusting inertia weight dynamically.The new weight is an exponential function of the minimal and maximal fitness of the particles,which is more conducive for the algorithm being out of local optimization in optimization process. Random factors are introduced to ensure population diversity,so that the particles converge to the global optimal position faster.The standard PSO,linearly decreasing inertia weigh (LDIW-PSO),mean adaptive inertia weigh (MAW-PSO) were tested and compared in different dimensions and population sizes through eight benchmark test functions.Experimental results show that the proposed EIW-PSO algorithm has faster convergence rate and higher solving precision.

Key words: Particle swarm optimization algorithm,Dynamically adjusting,Inertia weight,Exponential function

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