Publications · 2011 · Book chapter / collection
A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization
In Madani, Kurosh and Correia, António and Rosa, Agostinho and Filipe, Joaquim (ed.): Computational Intelligence, Springer Berlin / Heidelberg, 343: 179–192, 2011
Abstract
The behavior of modern meta-heuristics is directed by both, the variation operators, and the values selected for the parameters of the approach. Particle swarm optimization (PSO) is a meta-heuristic which has been found to be very successful in a wide variety of optimization tasks. In PSO, a swarm of particles fly through hyper-dimensional search space being attracted by both, their personal best position and the best position found so far within a neighborhood. In this paper, we perform a statistical study in order to analyze whether the neighborhood topology promotes a convergence acceleration in four PSO-based algorithms: the basic PSO, the Bare-bones PSO, an extension of BBPSO and the Bare-bones Differential Evolution. Our results indicate that the convergence rate of a PSO-based approach has a strongly dependence of the topology used. We also found that the topology most widely used is not necessarily the best topology for every PSO-based algorithm.
BibTeX
@incollection {Toscano11,
author = {Toscano-Pulido, Gregorio and Reyes-Medina, Angelina and Ramírez-Torres, José},
affiliation = {Information Technology Laboratory Scientific and Technological, CINVESTAV-Tamaulipas, Park TECNOTAM – Km. 5.5 carretera Cd. Victoria-Soto La Marina Cd. Victoria, Tamaulipas, 87130 Mexico},
title = {A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization},
booktitle = {Computational Intelligence},
series = {Studies in Computational Intelligence},
editor = {Madani, Kurosh and Correia, António and Rosa, Agostinho and Filipe, Joaquim},
publisher = {Springer Berlin / Heidelberg},
pages = {179-192},
volume = {343},
year = {2011},
doi = {10.1007/978-3-642-20206-3_12},
url = {http://dx.doi.org/10.1007/978-3-642-20206-3_12},
}