Enhanced Particle Swarm Optimization With Self-Adaptation Based On Fitness-Weighted Acceleration Coefficients

Hei Chia Wang, Che Tsung Yang

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

Acceleration coefficients are the control parameters used to tune the movements of cognition and social components in the particle swarm optimization (PSO) algorithm. Because most of the PSO algorithms treat individual particles equally despite the different positions of distinct particles, the inhomogeneous spread and scattering of the data samples during evolution are ignored. In this regard, the proposed PSO-FWAC algorithm aims to enhance the adaptability of individual particles by introducing the diverse acceleration coefficients according to their corresponding fitness values. The experimental results show that the PSO-FWAC outperforms the static and time-varying approaches.

原文English
頁(從 - 到)97-110
頁數14
期刊Intelligent Automation and Soft Computing
22
發行號1
DOIs
出版狀態Published - 2016 一月 2

All Science Journal Classification (ASJC) codes

  • 軟體
  • 理論電腦科學
  • 計算機理論與數學
  • 人工智慧

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