首页 | 本学科首页   官方微博 | 高级检索  
     检索      

蚁群算法研究进展
引用本文:马军建,董增川,王春霞,陈康宁.蚁群算法研究进展[J].河海大学学报(自然科学版),2005,33(2):139-143.
作者姓名:马军建  董增川  王春霞  陈康宁
作者单位:1. 河海大学水资源环境学院,江苏,南京,210098
2. 广东省水利电力勘测设计研究院,广东,广州,510170
基金项目:国家自然科学基金重大资助项目(50099620)
摘    要:人工蚁群算法是受到蚂蚁在觅食过程中能发现蚁巢到食物的最短路径这种搜索机制的启发而发展起来的一种群体智能算法、蚁群算法在求解一系列困难的组合优化问题上取得成效,成为解决TSP,VRP,QAP,JSP等典型问题的一种新型的强有力算法.对蚁群算法的起源和发展历史、算法理论研究的主要内容和方法、基于算法的改进以及应用范畴等,进行了系统的总结与综述,并对这一新型现代启发式算法的发展方向进行了展望.

关 键 词:蚁群算法  组合优化  人工蚁群  群集智能
文章编号:1000-1980(2005)02-0139-05
修稿时间:2005/4/12 0:00:00

Advances in research of ant colony algorithm
MA Jun-jian,DONG Zeng-chuan,WANG Chun-xia,CHEN Kang-ning.Advances in research of ant colony algorithm[J].Journal of Hohai University (Natural Sciences ),2005,33(2):139-143.
Authors:MA Jun-jian  DONG Zeng-chuan  WANG Chun-xia  CHEN Kang-ning
Institution:MA Jun-jian~1,DONG Zeng-chuan~1,WANG Chun-xia~2,CHEN Kang-ning~1
Abstract:The artificial Ant Colony Algorithm (ACA) is a new type of swarm intelligence algorithm with the ability to successfully achieve better solution to complicated combinatorial optimization problems than other popular metaheuristic algorithms. The algorithm takes inspiration from the observations of ant colonies foraging behavior with which ants can find the shortest paths from food sources to their nests. Research on ACA have revealed its potential to solve some classic combinatorial optimization problems, such as TSP, VRP, QAP, JSP, etc. The origin, the development process, and the methodologies of ACA were systematically reviewed, as well as its improvements and applications. Finally, expectation of future research on this new metaheuristic algorithm was presented.
Keywords:ant colony algorithm  combinatorial optimization  artificial ant colony  swarm intelligence
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《河海大学学报(自然科学版)》浏览原始摘要信息
点击此处可从《河海大学学报(自然科学版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号