An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic MapsReport as inadecuate

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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 178959, 17 pages -

Research ArticleSchool of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa

Received 12 September 2013; Accepted 9 December 2013; Published 20 January 2014

Academic Editor: Sergio Preidikman

Copyright © 2014 Akugbe Martins Arasomwan and Aderemi Oluyinka Adewumi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper experimentally investigates the effect of nine chaotic maps on the performance of two Particle Swarm Optimization PSO variants, namely, Random Inertia Weight PSO RIW-PSO and Linear Decreasing Inertia Weight PSO LDIW-PSO algorithms. The applications of logistic chaotic map by researchers to these variants have led to Chaotic Random Inertia Weight PSO CRIW-PSO and Chaotic Linear Decreasing Inertia Weight PSO CDIW-PSO with improved optimizing capability due to better global search mobility. However, there are many other chaotic maps in literature which could perhaps enhance the performances of RIW-PSO and LDIW-PSO more than logistic map. Some benchmark mathematical problems well-studied in literature were used to verify the performances of RIW-PSO and LDIW-PSO variants using the nine chaotic maps in comparison with logistic chaotic map. Results show that the performances of these two variants were improved more by many of the chaotic maps than by logistic map in many of the test problems. The best performance, in terms of function evaluations, was obtained by the two variants using Intermittency chaotic map. Results in this paper provide a platform for informative decision making when selecting chaotic maps to be used in the inertia weight formula of LDIW-PSO and RIW-PSO.

Author: Akugbe Martins Arasomwan and Aderemi Oluyinka Adewumi



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