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Detail
Journal Article
Enhancement of Particle Swarm Optimization by Stabilizing Particle Movement
- Authors
- Hyunseok Kim, Seongju Chang, Tae-Gyu Kang
- Issue Date
- 2013-12
- Citation
- ETRI Journal, v.35, no.6, pp.1168-1171
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- We propose an improvement of particle swarm optimization (PSO) based on the stabilization of particle movement (PM). PSO uses a stochastic variable to avoid an unfortunate state in which every particle quickly settles into a unanimous, unchanging direction, which leads to overshoot around the optimum position, resulting in a slow convergence. This study shows that randomly located particles may converge at a fast speed and lower overshoot by using the proportional-integralderivative approach, which is a widely used feedback control mechanism. A benchmark consisting of representative training datasets in the domains of function approximations and pattern recognitions is used to evaluate the performance of the proposed PSO. The final outcome confirms the improved performance of the PSO through facilitating the stabilization of PM. © 2013 ETRI.
- KSP Keywords
- Control mechanism, Fast speed, Feedback Control, Improved performance, Optimum position, Pattern recognition, Slow convergence, particle movement, stochastic variable