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Mathematical Problems in EngineeringVolume 2013 2013, Article ID 406047, 11 pages

Research Article

College of Business Administration, Hunan University, No. 11 Lushan South Road, Changsha 410082, China

Liverpool Business School, Liverpool John Moores University, Redmonts Building, Brownlow Hill, Liverpool L3 5UX, UK

Received 14 April 2013; Revised 22 September 2013; Accepted 7 October 2013

Academic Editor: T. Warren Liao

Copyright © 2013 Mi-Yuan Shan 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

We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization FCOCQPSO in vague sets IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.





Autor: Mi-Yuan Shan, Ren-Long Zhang, and Li-Hong Zhang

Fuente: https://www.hindawi.com/



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