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

• Published in: Mathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 11
• Main Authors: Shan, Mi-Yuan, Zhang, Li-Hong, Zhang, Ren-Long
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013; John Wiley & Sons, Inc
• Subjects: Algorithms; Benchmarking; Cloud computing; Cluster analysis; Clustering; Combinatorial analysis; Data analysis; Data mining; Datasets; Fault diagnosis; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Gene expression; Genetic algorithms; International conferences; Mathematical models; Mutation; Particle swarm optimization; Permutations; Swarm intelligence
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2013/406047

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.