Ameliorated particle swarm optimization by integrating Taguchi methods

Chuan-Hsi Liu, Yen Liang Chen, Jen Yang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this study, a novel particle swarm optimization (PSO) integrated with Taguchi method will be introduced. We use Taguchi method to assist PSO in finding the optimum in each dimension of position vectors during iterations, and exploit those optima to derive a new best-adaptive position vector (particle) afterward. Through verification over six benchmark functions, we have compared this PSO-Taguchi algorithm with the traditional global and local versions of PSO, and have found that the PSO-Taguchi method has a superior performance in convergence rate. In this paper, PSO will be first introduced. Then Taguchi method and its characteristics will be reviewed. Next, the issue of slow convergence speed with regard to the traditional PSO will be discussed. Finally, in order to solve this issue, a novel PSO-Taguchi algorithm will be proposed and verified through simulations.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages1823-1828
Number of pages6
DOIs
Publication statusPublished - 2010 Nov 15
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 2010 Jul 112010 Jul 14

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume4

Other

Other2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
CountryChina
CityQingdao
Period10/7/1110/7/14

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Keywords

  • Optimization technique
  • Particle swarm optimization (PSO)
  • Taguchi method

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Human-Computer Interaction

Cite this

Liu, C-H., Chen, Y. L., & Chen, J. Y. (2010). Ameliorated particle swarm optimization by integrating Taguchi methods. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 (pp. 1823-1828). [5580960] (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 4). https://doi.org/10.1109/ICMLC.2010.5580960