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

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

doi:10.1155/2013/406047

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.

Highlights

Clustering is a popular data analysis method and plays an important role in data mining
The COCQPSO and FCOCQPSO algorithms focus on the collective behaviors that result from the local interactions of the individuals and interactions with their environment
Algorithm is sensitive to initial value, the shortcomings of easy to fall into local optimum, this paper proposes a method of fuzzy clustering based on particle swarm optimization

Summary

Introduction

Clustering is a popular data analysis method and plays an important role in data mining. We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO) to solve the stochastic problem.

Results

Conclusion