Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 126970, 9 pages
http://dx.doi.org/10.1155/2008/126970
Research Article

A Strongly Interacting Dynamic Particle Swarm Optimization Method

Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria 0002, South Africa

Received 24 July 2007; Accepted 18 December 2007

Academic Editor: Riccardo Poli

Copyright © 2008 S. Kok and J. A. Snyman. 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

A novel dynamic interacting particle swarm optimization algorithm (DYN-PSO) is proposed. The algorithm can be considered to be the synthesis of two established trajectory methods for unconstrained minimization. In the new method, the minimization of a function is achieved through the dynamic motion of a strongly interacting particle swarm, where each particle in the swarm is simultaneously attracted by all other particles located at positions of lower function value. The force of attraction experienced by a particle at higher function value due to a particle at a lower function value is equal to the difference between the respective function-values divided by their stochastically perturbed position difference. The resultant motion of the particles under the influence of the attracting forces is computed by solving the associated equations of motion numerically. An energy dissipation strategy is applied to each particle. The specific chosen force law and the dissipation strategy result in the rapid collapse (convergence) of the swarm to a stationary point. Numerical results show that, in comparison to the standard particle swarm algorithm, the proposed DYN-PSO algorithm is promising.