A Novel Distributed Quantum-Behaved Particle Swarm Optimization. (3rd May 2017)

• Record Type: Journal Article
• Authors: Li, Yangyang; Chen, Zhenghan; Wang, Yang; Jiao, Licheng; Xue, Yu
• Other Names: Zhang Gexiang Academic Editor.
• Is Part Of: Journal of optimization. Volume 2017(2017)
• Publication Date: 2017-05-03
• Subjects: Mathematical optimization -- Periodicals; Structural optimization -- Periodicals; Mathematical optimization; Structural optimization; Periodicals; Electronic journals; 519.6
• DOI: 10.1155/2017/4685923
• Languages: English
• ISSNs: 2356-752X

Abstract

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.