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Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

Author

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  • Mi-Yuan Shan
  • Ren-Long Zhang
  • Li-Hong Zhang

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.

Suggested Citation

  • Mi-Yuan Shan & Ren-Long Zhang & Li-Hong Zhang, 2013. "Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, November.
    • Handle: RePEc:hin:jnlmpe:406047
    DOI: 10.1155/2013/406047
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