基于量子粒子群优化的属性约简

量子粒子群优化(QPSO)算法改进了粒子进化策略，使粒子具有更大搜索空间，可更好地避免陷入局部最优。该文将普通QPSO算法转化为二进制QPSO算法，提出基于QPSO优化的属性约简算法。实验结果表明，二进制QPSO算法的约简结果优于Hu算法和粒子群优化约简算法。

Attribute Reduction Based on Quantum-behaved Particle Swarm Optimization

Quantum-behaved Particle Swarm Optimization(QPSO) algorithm makes change for the evolutionary strategy of particles, and the particles has much broader search space, which helps to avoid falling into local optimal. This paper transforms QPSO into binary QPSO, and combines QPSO with attribute reduction algorithm, presents attribute reduction based on QPSO. Experimental results show that this algorithm can achieve much better reduction result than Hu algorithm and particle swarm optimization reduction algorithm.