By Topic

A Score Based Method for Controlling the Convergence Behavior of Particle Swarm Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chandra, S. ; Dept. of Comput. Sci. & Inf. Technol., Jaypee Univ. of Info. Technol., Solan ; Bhat, R. ; Chauhan, D.S.

In recent years, Particle Swarm Optimization (PSO) has been used in data mining, feature extraction and other optimization based applications. Time to time, a number of researchers have suggested modifications to the basic PSO. Although this optimization technique finds good solutions much faster than the traditional and evolutionary algorithms, they suffer from a major drawback of premature convergence. In addition, it has been found experimentally that the quality of the solutions does not improve as the number of iterations is increased. In this paper we discuss the reason behind the premature convergence. We present a new method based on performance-scoring for improving the algorithm The scoring based model is applied to the basic and some of the modified versions of PSO models.

Published in:

Computer Modelling and Simulation, 2009. UKSIM '09. 11th International Conference on

Date of Conference:

25-27 March 2009