[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(4):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(4):1-8.
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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(4):1-8.
基于信息素扩散模型解耦控制策略的蚁群算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
2
期数:
2007年第4期
页码:
1-8
栏目:
学术论文—人工智能基础
出版日期:
2007-08-25
- Title:
- An ant colony optimization algorithm based on a decouplingcontrol strategy of pheromone diffusion model
- 文章编号:
- 1673-4785(2007)04-0001-08
- 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 metaheuristic 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
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）.
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、Web智能、计算智能.
更新日期/Last Update:
2009-05-07