Summary
International Symposium on Nonlinear Theory and its Applications
2009
Session Number:C2L-C
Session:
Number:C2L-C1
Particle swarm optimization using a chaotic restarting method
Keiji Tatsumi, Tetsuzo Tanino,
pp.-
Publication Date:2009/10/18
Online ISSN:2188-5079
Summary:
The particle swarm optimization (PSO) method is a population-based optimization technique which searches for solutions by updating simultaneously a number of candidate solutions called particles. Since in PSO the exploration ability is important to find a desirable solution, various kinds of methods have been investigated to improve it. In this paper, we propose a restarting PSO model, where all particles are basically updated by the same dynamical system in the original PSO, while particles trapped at undesirable local minima are restarted by initializing their velocities and positions, and updates by the chaotic dynamical system with sinusoidal perturbations during a certain period. The restarted particles can not only escape from the undesirable local minimum but also search for solutions extensively by the chaotic behavior, while particles execute the detail search around the global best solution. Therefore, this model can be expected to keep a balance of intensification and diversification of the search. Through computational experiments, we verify the performance of the proposed model by applying it to some global optimization problems.