Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 827401, 9 pages
http://dx.doi.org/10.1155/2008/827401
Research Article

Novel Orthogonal Momentum-Type Particle Swarm Optimization Applied to Solve Large Parameter Optimization Problems

Department of Information Management, College of Electrical Engineering and Information Science, I-Shou University, No. 1, Section 1, Syuecheng Road, Kaohsiung County 840, Taiwan

Received 13 July 2007; Accepted 10 January 2008

Academic Editor: Jim Kennedy

Copyright © 2008 Jenn-Long Liu and Chao-Chun Chang. 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.

Abstract

This study proposes an orthogonal momentum-type particle swarm optimization (PSO) that finds good solutions to global optimization problems using a delta momentum rule to update the flying velocity of particles and incorporating a fractional factorial design (FFD) via several factorial experiments to determine the best position of particles. The novel combination of the momentum-type PSO and FFD is termed as the momentum-type PSO with FFD herein. The momentum-type PSO modifies the velocity-updating equation of the original Kennedy and Eberhart PSO, and the FFD incorporates classical orthogonal arrays into a velocity-updating equation for analyzing the best factor associated with cognitive learning and social learning terms. Twelve widely used large parameter optimization problems were used to evaluate the performance of the proposed PSO with the original PSO, momentum-type PSO, and original PSO with FFD. Experimental results reveal that the proposed momentum-type PSO with an FFD algorithm efficiently solves large parameter optimization problems.