A damping factor based particle swarm optimization approach

Mingfu He, Mingzhe Liu, Xin Jiang, Ruili Wang, Helen Zhou

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper proposes a novel damping factor based particle swarm optimization (DFPSO) to solve the large scale and high-dimensional searching space problems in terms of convergence to global optima. In this optimal searching strategy, we balance the exploring and exploiting ability of particles by introducing a new damping factor. Also, fuzzy c-means clustering is applied to cluster the particles' positions for the individuals' neighborhood establishment. Our comparative study about benchmark test functions demonstrates that the proposed variant of PSO outweighs the performance of standard PSO and three state-of-Art variants of PSO in terms of global optimum convergence and final optimal results.

Original languageEnglish
Title of host publicationProceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages13-18
Number of pages6
ISBN (Electronic)9781509065738
DOIs
Publication statusPublished - 2 Jul 2017
Externally publishedYes
Event9th International Conference on Modelling, Identification and Control, ICMIC 2017 - Kunming, China
Duration: 10 Jul 201712 Jul 2017

Publication series

NameProceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
Volume2018-March

Conference

Conference9th International Conference on Modelling, Identification and Control, ICMIC 2017
Country/TerritoryChina
CityKunming
Period10/07/1712/07/17

Keywords

  • Damping Factor
  • Fuzzy C-Means Clustering
  • Particle Swarm Optimization

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mechanics of Materials
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Biomedical Engineering
  • Computational Mechanics
  • Modelling and Simulation

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