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Parameter selection and performance study in particle swarm optimization
Published in -
Volume: 1298
Pages: 564 - 570
The present paper describes the Particle Swarm Optimization (PSO) technique and how different parameters in the algorithm may be selected in order to achieve faster convergence to the solution for a given optimization problem. PSO has become a common heuristic technique in the optimization community with many researchers exploring the concepts, issues and applications of the algorithm. PSO has undergone many changes since its introduction in 1995. As researchers have learnt about the technique, they have derived new versions, new applications and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of the particle swarming, including variations in the algorithm, current and ongoing research, and applications. In this paper we first analyze the impact that the inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provide guidelines for selecting these two parameters. © 2010 American Institute of Physics.
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