Next Article in Journal
A Polygon Model for Wireless Sensor Network Deployment with Directional Sensing Areas
Previous Article in Journal
Polyester Sulphonic Acid Interstitial Nanocomposite Platform for Peroxide Biosensor
Article Menu

Export Article

Open AccessReview
Sensors 2009, 9(12), 9977-9997; doi:10.3390/s91209977

A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization

1
Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India
2
School of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Korea
*
Author to whom correspondence should be addressed.
Received: 14 October 2009 / Revised: 18 November 2009 / Accepted: 1 December 2009 / Published: 9 December 2009
(This article belongs to the Section Chemical Sensors)
View Full-Text   |   Download PDF [482 KB, uploaded 21 June 2014]   |  

Abstract

The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov’s stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy. View Full-Text
Keywords: particle swarm dynamics; metaheuristics; continuous function optimization; stability; convergence; lyapunov stability theorem particle swarm dynamics; metaheuristics; continuous function optimization; stability; convergence; lyapunov stability theorem
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bhattacharya, S.; Konar, A.; Das, S.; Han, S.Y. A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization. Sensors 2009, 9, 9977-9997.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top