基于WS小世界模型的蝙蝠优化算法  

Modified Bat Algorithm Based on WS Small-world Model

作　　者：杨晓琴[1] YANG Xiao-qin(Taiyuan Radio and TV University,Taiyuan 030002,China)

机构地区：[1]太原广播电视大学,山西太原030002

出　　处：《计算机与现代化》2018年第8期28-34,共7页Computer and Modernization

基　　金：国家自然科学基金(青年科学基金)资助项目(61403272);山西省重点研发计划(工业部分)项目(201703D121042-1)

摘　　要：蝙蝠算法是在对微型蝙蝠回声观察研究的基础上发现蝙蝠回声和优化目标功能之间的关系而提出的一种新算法。蝙蝠算法具有强大的搜索性能,但是其局部搜索相对简单,个体间缺乏信息互通,搜索能力差。尽管目前也提出了一些相关改进算法,但高维优化方面较少涉及。考虑到蝙蝠群体中个体相互联系与作用的方式有动态复杂的感知网络结构,具有"小世界"特性,所以首先把有"小世界"特性的WS小世界模型引入蝙蝠算法,利用WS小世界模型断边重连的特点生成动态的邻域结构,这种邻域结构能够提高整体的搜索能力。实例验证表明借助一般的蝙蝠算法可以进行局部搜索。Bat algorithm is a new heuristic algorithm based on the observation and study of microbat echo,which is inspired by finding the relationship of bat echolocation behavior and the optimization objective function. Although the bat algorithm has powerful search performance,but it has a relatively simple local search mode and lacks of information sharing between individuals lead to bad search performance. Despite there are many improved algorithms proposed until now,few modified algorithms focus on high-dimensional optimization. Considering individuals have close relationship,which is complex network structure. The relationship is similar to small-world model. Therefore,WS small-world model is employed to optimize bat algorithm. Dynamic neighborhood structure is generated using the feature of WS small-world model edge breaking reconnection,which can improve the overall search capability. An example shows that the general bat algorithm can be used for local search.

关 键 词：蝙蝠算法 小世界模型 网络结构 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]