Title: Improved Particle Swarm Optimization Algorithm Based on Random Perturbations
Abstract: This paper proposed an novel improved particle swarm optimizer algorithm based on random perturbations (PSO-RP)with global convergence performance. Random perturbations are introduced to improve the performance of global convergence of the particle swarm optimizer (PSO). The novel search strategy enables the PSO-RP to make use of random information, in addition to experience, to achieve better quality solutions. Simulations show the novel random search strategy enables the PSO-RP to own the performance of global convergence. Five of well-known benchmarks used in evolutionary optimization methods are used to evaluate the performance of the PSO-RP. From experiments, we observe that the PSO-RP significantly improves the PSO's performance and performs better than the basic PSO and other recent variants of PSO.
Publication Year: 2010
Publication Date: 2010-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 2
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