By Topic

Competitive approaches to PSO algorithms via new acceleration co-efficient variant with mutation operators

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)

This paper presents a few new competitive approaches to particle swarm optimization (PSO) algorithm in terms of the global and local best values (GLbest-PSO) and the standard PSO along with three set of variants namely, inertia weight (IW), acceleration co-efficient (AC) and mutation operators in this paper. Standard PSO is designed with time varying inertia weight (TVIW) and either time varying AC (TVAC) or fixed AC (FAC) while GLbest-PSO comprises of global-average local best IW (GaLbestIW) with either global-local best AC (GLbestAC) or FAC. The performances of these two algorithms are improved considerably in solving an optimal control problem, by introducing the concept of mutation variants between particles in each generation. The presence of mutation operator sharpens the convergence and tunes to the best solution. In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate the improved performances of the proposed PSOs over the standard PSOs.

Published in:

Computational Intelligence and Multimedia Applications, 2005. Sixth International Conference on

Date of Conference:

16-18 Aug. 2005