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。
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.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第1期45-47,共3页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60672137)
高等院校博士学科点专项科研基金(theChina Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20060497015)。
关键词
蚁群算法
信息素更新规则
变异
TSP
ant colony algorithm
rule of updating pheromones
mutation
TSP
