关键词: 属性约简, 粒子群优化算法, 量子行为, 云模型

Abstract: In the processing information system, the particle swarm optimization algorithm is applied for the minimum attribute reduction, which is slow and easy to fall into local optimum. Accordingly, this paper proposes a quantum-behaved particle swarm optimization algorithm combined with cloud model（CQPSO） to reduce the number of attributes in data set. First, the speed of convergence is accelerated by using a quantum behavior of QPSO algorithm; and the cloud model is introduced into QPSO to control different particle swarms in different states; then, the attribute reduction mathematical model is constructed according to property dependency and other properties; finally, the CQPSO algorithm is used to solve the problem and achieve the reduction results. In this experiment, the CQPSO algorithm is simulated and compared by the standard test function, which shows that the CQPSO algorithm performance is better than the quantum-behaved PSO algorithm. And the UCI standard database is used to perform attribute reduction tests. The results show that the proposed attribute reduction method is superior to the existing reduction method, and its calculation speed is fast and the recognition precision is high.

Key words: attribute reduction, particle swarm optimization algorithm, quantum behavior, cloud model