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Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 831-835
ABSTRACT
Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is a new variant of Particle Swarm Optimization (PSO). It is also a population-based search strategy, which has good performance on well-known numerical test problems. QPSO is based on the standard PSO and inspired by the theory of quantum physics. In this paper, we explore the parallelism of QPSO and implement the parallel QPSO based on the Neighborhood Topology Model, which is much closer to the nature world. The performance of the parallel QPSO is compared to PSO and QPSO on a set of benchmark functions. The results show that the parallel QPSO outperforms the other two algorithms.
INDEX TERMS
Quantum-behaved PSO, Neighborhood Topology Model, Parallel Computing
CITATION
Xiaogen Wang, Jun Sun, Wenbo Xu, "A Parallel QPSO Algorithm Using Neighborhood Topology Model", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 831-835, doi:10.1109/CSIE.2009.674
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