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交互变邻域微分进化群搜索优化算法 被引量：1

Interactive Dynamic Neighborhood Differential Evolutionary Group Search Optimizer
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摘要 群搜索优化算法(Group Search Optimizer,GSO)具有广泛的生物学背景,特别是引入动物的视觉搜索机制,并且同一些已有的群智能算法相比较,在高维多峰问题上有更好的效果.但算法在个体觅食策略的选择上以及整个动物群体间信息共享的网络拓扑结构来看,存在错过最优值和信息交流模式过于简单的缺陷.受NW模型的启发,同时采用动态采样的方式提出了交互变邻域微分进化群搜索优化算法(Interactive Dynamic Neighborhood Differential Evolutionary GSO,IDGSO),并采用均匀设计和线性回归方法对参数进行选择,4个标准测试函数表明了IDGSO的有效性. Group Search Optimizer （GSO） has the advantage of the design from a biological view, while animal scanning mechanisms are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. Compared with some existing group intelligence algorithms, it has a better effect in the high dimensional problems. But from the individual foraging strategies it choose and the entire animal groups information sharing network topology, global optimal possibly missed and information exchange model is simple. Inspiration from the Newman and Watts model, Interactive Dynamic Neighborhood GSO （ IDGSO ） is proposed based on dynamic sampling. Adopting uniform designand the linear regression method on the parameter selection, 4 benchmark functions demonstrate the effectiveness of the algorithm.
作者 贺国华 崔志华 谭瑛
机构地区 太原科技大学复杂系统与计算智能实验室
出处 《小型微型计算机系统》 CSCD 北大核心 2012年第4期809-814,共6页 Journal of Chinese Computer Systems
基金 教育部科学技术研究重点项目(209021)资助 国家青年科学基金项目(61003053)资助
关键词 群搜索优化算法 微分模型 动态采样 拓扑结构 均匀设计 group search optimizer differential model dynamic sampling network topology uniform design
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献18

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共引文献11

同被引文献16

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小型微型计算机系统
2012年 第4期

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