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


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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.


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Pages

Total Pages: 14
Pages: 97-110

DOI
10.1080/10798587.2015.1057956


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Published

Volume: 22
Issue: 1
Year: 2015

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References

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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