[1]冀俊忠,刘椿年,黄 振.基于信息素扩散模型解耦控制策略的蚁群算法[J].智能系统学报,2007,2(04):1-8.
 JI Jun-zhong,LIU Chun-nian,HUANG Zhen.An ant colony optimization algorithm based on a decouplingcontrol strategy of pheromone diffusion model[J].CAAI Transactions on Intelligent Systems,2007,2(04):1-8.
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基于信息素扩散模型解耦控制策略的蚁群算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第2卷
期数:
2007年04期
页码:
1-8
栏目:
出版日期:
2007-08-25

文章信息/Info

Title:
An ant colony optimization algorithm based on a decouplingcontrol strategy of pheromone diffusion model
文章编号:
1673-4785(2007)04-0001-08
作者:
冀俊忠刘椿年黄 振
北京工业大学计算机学院,北京100022
Author(s):
JI Jun-zhong LIU Chun-nian HUANG Zhen
College of Computer Science and Technology, Beijing University of Technology, Beijing 100022, China
关键词:
蚁群算法扩散模型耦合性解耦控制策略
Keywords:
ant colony optimization diffusion model coupling characteristic de coupling control strategy
分类号:
TP18
文献标志码:
A
摘要:
蚁群优化是一种元启发式的随机搜索技术.信息素是蚁群进行交流并实现群集智能的媒介,所以信息素的更新策略一直是蚁群算法中的一个研究热点.针对信息素扩散的耦合特征,提出一种基于信息素扩散模型解耦控制策略的蚁群算法.对信息素扩散模型进行改善,建立以蚂蚁经过的路径(直线段)为信源的信息素扩散模型,通过分析信息素扩散浓度场的耦合性,引入去耦控制策略来修正信息素的更新公式,大量TSP (traveli ng salesman problem)问题的实验表明:该算法不仅能获得更好的解,而且能加快算法的收敛速度.
Abstract:
Ant colony optimization (ACO) is a metaheuristic search technique. P h eromones are an important media ants use to communicate with each other and impl ement swarm intelligence. Thus research on pheromone updating strategies is a ho tspot in ACO. A new decoupling control strategy model of pheromone diffusion is proposed based on the coupling characteristic of pheromone diffusion. First, the algorithm sets up a new pheromone diffusion model with the path that the ant tr avels as the pheromone source. Then, according to the coupling degree of the con centration field of pheromone diffusion, a decoupling control strategy is employ ed to revise the pheromone updating formula. Experimental results for many TSP p roblems demonstrate that the proposed algorithm can not only generate better sol utions but also accelerate the speed of convergence. 

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2006-12-28.
基金项目:
国家自然科学基金资助项目(60496322);
北京市教育委员会科技发展资助项目(KM200610005020);
北京市委组织部优秀人才培养资助项目(20061C0501500190)
作者简介:
冀俊忠,男,1969年生,博士,副研究员,中国计算机学会高级会员,主要研究方向为机器学习、Web智能、计算智能.在国内外学术刊物和重要国际学术会议上发表论文20余篇,其中10余篇被SCI、EI、ISTP三大检索收录.
 E-mail: jjz01@bjut.edu.cn. 
刘椿年,男,1944年生,教授,博士生导师,主要研究方向为数据挖掘、人工智能、约束逻辑程序设计.主持多项国家自然科学基金和863计划课题.在国内外学术刊物和重要国际学术会议上发表论文100余篇,出版专著及译著多部.黄 振,男,1981年生,硕士研究生,主要研究方向为机器学习、W eb智能、计算智能.
更新日期/Last Update: 2009-05-07