Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (04): 78-83.doi: 10.12052/gdutxb.160079

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An Improved Particle Swarm Algorithm Far Away from Worst Solution

Liu Wen-kai, Wen Jie-chang   

  1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2016-06-12 Online:2017-07-09 Published:2017-07-09

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Abstract:

A new particle swarm optimization (PSO) algorithm is proposed in reference to worst solution of being local in standard PSO to avoid the problems of being easy to converge to a local minimum, premature convergence and low precision. In the late period of particle swarm optimization and global optimum (Gbest) when there is no change, the local optimum (Pbest) is equal to the Gbest. Then the speed to move closer to the optimal direction vector is zero, and particles go along the direction only by its own inertia. When the proposed algorithm is added to the local minimum (Pworst) algorithm, the particle's forward move is not only affected by its own inertia, but it also can continue to find the best, so as to find the global optimal. This algorithm, by applying the idea of the particles avoiding the worst local and global solution, improves the velocity updating formula based on the PSO, and tests respectively the worst global solution and local worst solution on the influence of particle swarm optimization algorithm. Several typical test function simulations show that the proposed algorithm not only has great advantages of convergence property over some other modified PSO algorithms, but is also effective in avoiding being trapped in local optimal solution.

Key words: particle swarm optimization (PSO), global worst

CLC Number: 

  • TP301

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