Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19201
Title: Paralled multi-Population particle swarm optimization algorithm for uncapacitated facility location problem using OpenMP
Authors: Wang, D
Wu, CH
Ip, A 
Wang, D
Yan, Y
Issue Date: 2008
Source: Evolutionary Computation, 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), IEEE Congress on : date, 1-6 June, 2008, Hong Kong, p. 1214-1218
Abstract: Parallel multi-population particle swarm optimization (PSO) algorithm using OpenMP is presented for the uncapacitated facility location (UFL) problem. The parallel algorithm performed asynchronously by dividing the whole particle swarm into several sub-swarms and updated the particle velocity with a variety of local optima. Each sub-swarm changes its best position so far of to its neighbor swarm after certain generations. The parallel multi-population PSO (PMPSO) algorithm is applied to several benchmark suits collected from OR-library. And the results are presented and compared to the result of serial execution multi-population PSO. It is conducted that the parallel multi-population PSO is time saving, especially for large scale problem and generated more robust results.
Keywords: Facility location
Parallel algorithms
Particle swarm optimisation
Publisher: IEEE
ISBN: 978-1-4244-1822-0
DOI: 10.1109/CEC.2008.4630951
Appears in Collections:Conference Paper

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