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Parallel multi-population Particle Swarm Optimization Algorithm for the Uncapacitated Facility Location problem using OpenMP

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5 Author(s)
Dazhi Wang ; School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110004 China ; Chun-Ho Wu ; Andrew Ip ; Dingwei Wang
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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.

Published in:

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-6 June 2008