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Quantized Particle Swarm Optimization: An Improved Algorithm Based on Group Effect and Its Convergence Analysis

[+] Author Affiliations
Zhenyi Liu, Sagar Deshpande, Qing Hui

Texas Tech University, Lubbock, TX

Paper No. DSCC2011-6055, pp. 321-327; 7 pages
doi:10.1115/DSCC2011-6055
From:
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1
  • Arlington, Virginia, USA, October 31–November 2, 2011
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5475-4
  • Copyright © 2011 by ASME

abstract

In this article a new simple-structure variation of Particle Swarm Optimization (PSO) algorithm is proposed. Since the standard PSO has a very good performance, the new variation retains many properties of standard PSO such as stochasticity and some other properties similar to those in Evolutionary Algorithms. However, unlike the standard PSO algorithm, in the new algorithm the particles can not only communicate with each other via the objective function but, via a new variable named “quantizer”, and hence, the new algorithm is labeled as the Quantized Particle Swarm Optimization algorithm. In addition, extensive simulations are given to show the advantages of the new algorithm over the standard PSO. Finally, the convergence analysis for deterministic version of the new algorithm is also presented.

Copyright © 2011 by ASME

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