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
Keywords: Facility location
Parallel algorithms
Particle swarm optimisation
Issue Date: 2008
Publisher: IEEE
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 How to cite?
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.
URI: http://hdl.handle.net/10397/19201
ISBN: 978-1-4244-1822-0
DOI: 10.1109/CEC.2008.4630951
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

16
Last Week
10
Last month
Citations as of Oct 16, 2017

WEB OF SCIENCETM
Citations

8
Last Week
1
Last month
0
Citations as of Oct 17, 2017

Page view(s)

50
Last Week
0
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.