计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (1): 45-47.

• 学术探讨 • 上一篇    下一篇

基于信息素适量更新与变异的高效蚁群算法

龚本灿1,2,李腊元2,蒋廷耀1,汪祥莉2   

  1. 1.三峡大学 电气信息学院,湖北 宜昌 443002
    2.武汉理工大学 计算机学院,武汉 430063
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-01-01 发布日期:2008-01-01
  • 通讯作者: 龚本灿

Efficient ant colony algorithm based on right pheromone updating and mutation

GONG Ben-can1,2,LI La-yuan2,JIANG Ting-yao1,WANG Xiang-li2   

  1. 1.College of Electrical Engineering and Information Technology,China Three Gorges University,Yichang,Hubei 443002,China
    2.College of Computer Science and Technology,Wuhan University of Technology,Wuhan 430063,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-01-01 Published:2008-01-01
  • Contact: GONG Ben-can

摘要: 为了克服基本蚁群算法求解速度慢、易于出现早熟和停滞现象的缺陷,提出了一种高效的蚁群算法(EACA)。它修改了基本蚁群算法中信息素的更新规则,使得每轮搜索后信息素的增量能更好地反映解的质量,以加快收敛;另外,它采用了一种启发式变异方法对路径进行优化,以产生搅动效应,避免早熟。以TSP问题为例进行的实验结果表明:提出的算法优于ACA和ACAGA。

关键词: 蚁群算法, 信息素更新规则, 变异, TSP

Abstract: To overcome the default of slow convergence speed,precocity and stagnation in the basic ant colony Algorithm(ACA),we proposed an Efficient Ant Colony Algorithm(EACA).It modifies the rule of updating pheromones in ACA,so that after every round of search,the increment of pheromone can better reflect the quality of a solution to quicken the convergence;In addition,it uses the heuristic mutation to optimize tours,generate disarrangement effect and avoid precocity.Experimental results for solving TSP(Traveling Salesman Problem) show that the proposed algorithm outperforms ACA and ACAGA.

Key words: ant colony algorithm, rule of updating pheromones, mutation, TSP