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题名: 群体智能理论研究及其在数据挖掘中的应用
其他题名: Research on Swarm Intelligence and Clustering
作者: 王玫
导师: 朱云龙
分类号: TP18
关键词: 群体智能 ; 聚类 ; SWARM仿真 ; ACO
索取号: TP18/W34/2005
学位专业: 模式识别与智能系统
学位类别: 硕士
答辩日期: 2005-05-31
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 先进制造技术研究室
中文摘要: 以大自然为师,常常能够启迪人的思维,进行科学创新。模仿生物的特殊本领,利用对生物的结构和功能原理的仿生,人们研制机械或各种新技术,如飞机的发明便受了鸟类飞行的启发。生物学家对动物的群体行为,特别是群体性昆虫如蚂蚁等,进行了长久的观察和研究,发现了很多有趣的现象,是仅仅由个体无法表现出来的,受这些集体行为的启发,设计算法用来进行分布式问题的求解,或者对群体的行为进行建模,产生了群体智能。数据挖掘是一门重于实践的学科,利用了机器学习、统计、数据库等多种技术进行知识的挖掘,为决策提供支持。 群体智能从90年代以来得到了发展,逐步成为科学研究的一个热点,在组合优化问题、机器人协作、知识发现等领域显示出应用的潜力。本文的研究目的,是对群体智能研究进行较为全面的总结,并探索其在数据挖掘上的应用。本文工作主要包括以下三个方面: 首先是群体智能的综述性研究,包括群体智能算法和群体智能模型两个部分。从几种生物的群体行为谈起,包括蚂蚁觅食、协作搬运、清扫巢穴,鸟类飞行、鱼群游动,对由此得到的典型算法—蚂蚁优化算法(ACO)和粒子群算法(PSO)—进行了总结和分析。在群体智能模型方面,将AER模型、回声模型、受限生成过程、筑巢模型等四种模型统一于群体智能。并提出了群体智能系统的三大本质特征:自组织、涌现和间接通信。 其次,试图通过一个仿真模型来展现和理解涌现现象。利用SWARM平台,模拟了狼群捕食的行为。期望通过主体之间的非直接通信的合作来更好的实现目标。 最后,本文在前人工作基础上,研究了群体智能在数据挖掘中的应用。聚类分析是数据挖掘中的一个重点,对基于群体智能的聚类算法提出了改进,试图提高群体智能聚类模型的实用性。 总之,本文对群体智能进行了较为全面的探索和尝试,群体优化算法、建模和应用等,对群体智能研究有一定的理论意义。
英文摘要: Human have got myriad heuristics from replicating nature’s way. Nature can always enlighten men on new inventions. With the utilization of bionics many mechanics are invented. For example, the invention of plane is originated from flying birds. Not always but very often the result of biological evolution is also the optimum solution for the engineer. Biologists have been studying on collective behaviors of creatures, especially of social insects such as ants and wasps. Many interesting behaviors that a single individual can not show have been observed. Based on these behaviors, typical algorithms have been proposed and used to solve distributed problems. Since 1990s swarm intelligence has been developed fast and showed potential in combinatorial optimization, robotics, and knowledge discovery and so on. The world is overwhelmed with data. Data mining, which is based on techniques of machine learning, database, and statistics, is very practical. The contributions of this dissertation are as follows: Firstly, a survey of swarm intelligence is given. Based on the behaviors of several creatures’ behaviors, the typical algorithms including Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are analyzed. Secondly,Four models that relate to swarm intelligence including AER, Echo, Constraint Generation Procedure and nest-building model are summarized and unified. Three essential characteristics of swarm intelligence are summarized. To explore the mechanism of emergence we try to build a model. SWARM is a platform that is designed for multi-agent. On this platform we studied cooperation of wolves when preying. Clustering is very important in data mining research. In this paper, how to combine Swarm intelligence and data mining is discussed. On the basis of the BM (basic model), a clustering algorithm based on Swarm intelligence is proposed and researched. In conclusion, the dissertation gives comprehensive survey and research on Swarm intelligence.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9569
Appears in Collections:工业信息学研究室_先进制造技术研究室_学位论文

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Recommended Citation:
王玫.群体智能理论研究及其在数据挖掘中的应用.[硕士学位论文].中国科学院沈阳自动化研究所.2005
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