doi:

DOI: 10.3724/SP.J.1016.2011.00115

Chinese Journal of Computers (计算机学报) 2011/34:1 PP.115-130

An Improved Particle Swarm Optimization Algorithm with Search Space Zoomed Factor and Attractor


Abstract:
To control particles to fly inside search space and deal with the problems of slow convergence speed and premature convergence of particle swarm optimization (PSO) algorithm, this paper studies the movement of particles and stability analysis of canonical PSO algorithm and proposes an improved PSO algorithm, called PSO with search space zoomed factor and attractor (SzAPSO), where search space zoomed factor is a key parameter to control the original search space to zoom in and out, which is benefit to retain the connection of particles’ position, reduce the subjective interference, and enforce the ability of global search, and attractor is a weighted average of global best and personal best for the normal particles except the global-best particle, which utilizes known information to enhance the power of local search and escaping from an inferior local optimum. SzAPSO is not only a kind of boundary condition, but also an effective PSO algorithm. Experimental studies show that SzAPSO algorithm proposed in this paper is more effective to do with errant particles, furthermore, improves greatly the convergence speed and accuracy, and obtains the admirable optimization results with smaller population size and evolution generations independent of the problem dimension and the location of the global optimum with respect to search space boundary.

Key words:swarm intelligence,particle swarm optimization,search space,boundary condition,attractor

ReleaseDate:2014-07-21 15:44:31



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