| 基于量子粒子群算法的聚类分析方法 |
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| 引用本文： | 叶安新,金永贤.基于量子粒子群算法的聚类分析方法[J].计算机工程与应用,2012,48(32):52-55,97. |
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| 作者姓名： | 叶安新 金永贤 |
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| 作者单位： | 1. 浙江师范大学行知学院,浙江金华,321003
2. 浙江师范大学数理与信息工程学院,浙江金华,321003 |
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| 基金项目： | 浙江省教育厅科研基金资助项目（No.Y201017073）. |
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| 摘 要： | 针对K-均值聚类方法受初始聚类中心影响,容易陷入局部最优解的问题,提出一种基于量子粒子群算法的聚类方法,该方法引入了动态调整量子门旋转角和量子变异操作,采用改进的变异算子,使粒子群体保持品种的多样性和优良性,避免陷入局部最优,同时结合粒子群优化算法,增加粒子群的全局搜索能力。仿真实验表明该方法在全局寻优能力和收敛效率上都有所提高。
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| 关 键 词： | 量子 粒子群优化(PSO) 聚类 K-均值 |
Clustering method based on quantum particle swarm optimization. |
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| YE Anxin
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JIN Yongxian.Clustering method based on quantum particle swarm optimization.[J].Computer Engineering and Applications,2012,48(32):52-55,97. |
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| Authors: | YE Anxin JIN Yongxian |
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| Affiliation: | 1 .Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321003, China 2.College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321003, China |
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| Abstract: | Aimed at the existing defects of traditional K-means, which is heavily dependent on the initial clustering center, and easy to trap into the local minimum, a new quantum particle swam optimization clustering method is pro- posed. The method introduces dynamic adjustment of quantum gate angle, quantum mutation operation, which can maintain the diversity and quality of varieties of the particles, avoid being trapped in local optimum. Furthermore, it combines with particle swarm optimization increasing the particle swarm＇ s global search capability. Simulation test results show that the proposed method imoroves the ~lobal ontimal ahilitv and th~ ,-cm ,~ |
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| Keywords: | quantum Particle Swarm Optimization（PSO） cluster K-means |
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