计算机科学与探索 ›› 2007, Vol. 1 ›› Issue (1): 87-94.

• 学术研究 • 上一篇    下一篇

蚁群算法中信息素增量和扩散模型的研究*

冀俊忠,刘椿年,黄 振   

  1. 北京工业大学 计算机学院 多媒体与智能软件技术北京市重点实验室,北京 100022
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-06-06 发布日期:2007-06-06
  • 通讯作者: 冀俊忠

Research of pheromone increment and diffusion model for ant colony optimization algorithms

JI Jun-zhong,LIU Chun-nian,HUANG Zhen

  

  1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology,College of Computer Science and Technology,Beijing University of Technology,Beijing 100022,China

  • Received:1900-01-01 Revised:1900-01-01 Online:2007-06-06 Published:2007-06-06
  • Contact: JI Jun-zhong

摘要:

文章提出一种新的基于信息素增量和扩散模型的蚁群算法。首先,基于能量守恒与转换定律对信息素的增量模型进行修正,以体现蚂蚁在不同路径上行走时所产生的信息量差异;其次,以蚂蚁经过的路径(直线段)作为信息素扩散浓度场的信源,改善了信息素扩散模型,强化了蚂蚁间的协作和交流。大量TSP(Traveling 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。

关键词: 蚁群算法, 能量守恒与转换, 扩散模型, 浓度场

Abstract:

A new ACO(Ant Colony Optimization) algorithm based on pheromone increment and diffusion models is presented.First,in light of energy conversation and transform,the pheromone increment formula is revised to show the pheromone difference of different paths.Meanwhile,a pheromone diffusion model based on info fountain of a path is established to faithfully reflect the strength field of pheromone diffusion,it strengthens the collaboration among ants.The experimental results for many TSP problems demonstrate that the proposed algorithm can not only get much better solutions but also improve the speed of convergence.

Key words: ant colony optimization, energy conversation and transform, diffusion model, strength field