Abstract
Standard Particle Swarm Optimization(PSO) algorithm falls into local optima easily and has low convergence accuracy when it is used to address the problem of complex functions optimization.In order to overcome the shortcomings,an improved PSO algorithm was proposed.The proposed algorithm integrated the attraction-repulsion mechanism in the field of biology into PSO algorithm and took full advantage of the mutual influence between particles to modify velocity updating formula,and thus maintained population diversity and enhanced the ability of particle to escape from the local optima.The experimental results demonstrate that the proposed algorithm outperforms two existing variants of the PSO algorithm in terms of convergence accuracy while improving the velocity of convergence in the later evolution phase and avoiding premature convergence problem effectively.
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