JACIII Vol.12 No.3 pp. 284-289
doi: 10.20965/jaciii.2008.p0284


Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

Jenn-Long Liu

Department of Information Management, I-Shou University, 1, Section 1, Hsueh-Cheng Rd., Ta-Hsu Hsiang, Kaohsiung County, Taiwan 840, Taiwan

April 15, 2007
September 22, 2007
May 20, 2008
evolving PSO, genetic algorithm, cognitive and social learning rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

Cite this article as:
Jenn-Long Liu, “Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.3, pp. 284-289, 2008.
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