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Journal of Optimization
Volume 2017 (2017), Article ID 4685923, 9 pages
https://doi.org/10.1155/2017/4685923
Research Article

A Novel Distributed Quantum-Behaved Particle Swarm Optimization

1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an, Shaanxi Province 710071, China
2School of Computer and Software, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China

Correspondence should be addressed to Yangyang Li; moc.361@197_yyl

Received 29 December 2016; Accepted 4 April 2017; Published 3 May 2017

Academic Editor: Gexiang Zhang

Copyright © 2017 Yangyang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Quantum-behaved particle swarm optimization (QPSO) is an improved version of particle swarm optimization (PSO) and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO) which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.