A Strongly Interacting Dynamic Particle Swarm Optimization Method. (31st March 2008)

• Record Type: Journal Article
• Authors: Kok, S.; Snyman, J. A.
• Other Names: Poli Riccardo Academic Editor.
• Is Part Of: Journal of artificial evolution and applications. Volume 2008(2008)
• Publication Date: 2008-03-31
• Subjects: Evolutionary programming (Computer science) -- Periodicals; Evolutionary programming (Computer science); Periodicals; Electronic journals; 006.3823
• DOI: 10.1155/2008/126970
• Languages: English
• ISSNs: 1687-6229

Abstract

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.