| 基于K-均值聚类的协同进化粒子群优化算法 |
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| 引用本文： | 王燕燕,葛洪伟,杨金龙,王娟娟.基于K-均值聚类的协同进化粒子群优化算法[J].计算机工程与应用,2015,51(22):61-65. |
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| 作者姓名： | 王燕燕 葛洪伟 杨金龙 王娟娟 |
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| 作者单位： | 1.江南大学 物联网工程学院，江苏 无锡 214122
2.国网潍坊供电公司，山东 潍坊 261021 |
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| 摘 要： | 针对粒子群优化（PSO）算法优化高维问题时，易陷入局部最优，提出一种基于K-均值聚类的协同进化粒子群优化（KMS-CCPSO）算法。该算法通过引入K-均值算法扩大种群的局部搜索范围，采用柯西分布和高斯分布相结合的方法更新粒子的位置。实验结果表明，该算法具有较好的优化性能，其优势在处理高维问题上更为明显。
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| 关 键 词： | 协同进化 K-均值 高维优化 粒子群优化 局部最优 |
Cooperatively coevolving particle swarms optimization on k-means cluster algorithm |
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| WANG Yanyan,GE Hongwei,YANG Jinlong,WANG Juanjuan.Cooperatively coevolving particle swarms optimization on k-means cluster algorithm[J].Computer Engineering and Applications,2015,51(22):61-65. |
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| Authors: | WANG Yanyan GE Hongwei YANG Jinlong WANG Juanjuan |
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| Affiliation: | 1.Department of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
2.State Grid Weifang Power Supply Company, Weifang, Shandong 261021, China |
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| Abstract: | Aimed at particle swarm optimization（PSO） algorithm is easy to fall into local optimal problems for optimizing a high-dimensional population, a new cooperative coevolving particle swarm optimization on K-means cluster（KMS-CCPSO） algorithm is put forward. In the proposed algorithm, the subspace of local search range is designed by K-means algorithm, and the new points’ position and velocity in the search space is relied on Cauchy and Gaussian distributions. The experimental results suggest that the proposed algorithm has better optimization performance, its advantage on the large-scale population optimization problem is more apparent. |
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| Keywords: | cooperative co-evolution k-means high-dimensional optimization particle swarm optimization local optimal |
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