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

Hei Chia Wang, Che Tsung Yang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)97-110
Number of pages14
JournalIntelligent Automation and Soft Computing
Volume22
Issue number1
DOIs
Publication statusPublished - 2016 Jan 2

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Enhanced Particle Swarm Optimization With Self-Adaptation Based On Fitness-Weighted Acceleration Coefficients'. Together they form a unique fingerprint.

Cite this