基于高聚集性无标度网络模型的微粒群算法 |
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引用本文: | 焦长义,穆华平,李静.基于高聚集性无标度网络模型的微粒群算法[J].复杂系统与复杂性科学,2010,7(1):82-87. |
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作者姓名: | 焦长义 穆华平 李静 |
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作者单位: | 1. 鹤壁职业技术学院电子与信息工程系,河南,鹤壁,458030 2. 烟台南山学院软件工程学院,山东,龙口,265713 |
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摘 要: | 受无标度网络结构特性的启发,将BA模型的"择优连接"机制进行扩展,引入微粒群群体组织方式的构造过程,提出基于高聚集性的无标度网络模型的微粒群算法。算法初期微粒被随机分布在环形结构中,随着搜索的进行不断增加新的微粒,并依据节点度和节点间的距离增加新的连接,最终形成具有高聚集性的无标度网络模型。这样,群体中多数微粒进行局部范围的搜索,而少量微粒按照全局模式搜索,两种方式相互制衡。仿真实验表明,改进后的算法能获得更好的收敛精度和进化速度。
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关 键 词: | 微粒群算法 无标度网络模型 择优连接 高聚集性 |
Particle Swarm Optimization with Highly-Clustered Scale-Free Network Model |
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Authors: | JIAO Chang-yi MU Hua-ping LI Jing |
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Institution: | JIAO Chang-yi1,MU Hua-ping1,LI Jing2(1.Hebi Vocation and Technology College,Hebi 458030,China,2.Software Engineering Institute,Yantai Nanshan University,Longkou 465713,China) |
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Abstract: | Enlightened by the properties of scale-free network model,preferential attachment mechanism of the BA model is extended and introduced into particle swarm optimization,and a novel particle swarm optimization with highly-clustered scale-free network model(PSO-HCSF) is proposed.At the early stage of the algorithm,particles were randomly distributed in a ring,new particles are continuously added into the population with searching,and based on the node degree and the distance between nodes new connections are p... |
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Keywords: | particle swarm optimization scale-free network model preferential attachment high cluster |
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