¹School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
²Key Laboratory of Gansu Advanced Control for Industrial Process, Lanzhou, China

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Abstract—The particle swarm optimization algorithm (PSO) has two typical problems as in other adaptive evolutionary algorithms, which are based on swarm intelligence search. To deal with the problems of the slow convergence rate and the tendency to trap into premature, an improved particle swarm optimization with decline disturbance index (DDPSO) is presented in this paper. The index was added when the velocity of the particle is prone to stagnation in the middle and later evolution periods. The modification improves the ability of particles to explore the global and local optimization solutions, and reduces the probability of being trapped into the local optima. Theoretical analysis, which is based on stochastic processes, proves that the trajectory of particle is a Markov processes and DDPSO algorithm converges to the global optimal solution with mean square merit. Experimental simulations show that the improved algorithm can not only improve the convergence of the algorithm significantly, but also avoid trapping into local optimization solution.

Index Terms—particle swarm optimization, premature, stochastic processes, decline disturbance index, convergence

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Cite: Fuqing Zhao, Jianxin Tang, Jizhe Wang, Chunmiao Wei, "An Improved PSO Algorithm with Decline Disturbance Index," Journal of Computers vol. 6, no. 4, pp. 691-697, 2011.