自适应蚁群算法求解最短路径和TSP问题  

Solving of Shortest Path Problem and TSP with Adaptive Ant Colony Algorithm

作　　者：易正俊[1] 李勇霞[1] 易校石 

机构地区：[1]重庆大学数学与统计学院,重庆401331 [2]重庆师范大学数学科学学院,重庆401131

出　　处：《计算机技术与发展》2016年第12期1-5,共5页Computer Technology and Development

基　　金：国家自然科学基金资助项目(69674012);重庆市科技攻关计划(CSTC2009AC3037)

摘　　要：对传统蚁群算法的初始化信息素浓度加入方向引导,避免蚁群在初始阶段盲目地随机搜索浪费较多的时间;在全局信息素更新过程中加入双曲正切函数作为动态因子,自适应地更新每次迭代较优解路径的信息素浓度,增大算法获取全局最优解的可能性。两个算例采用改进的蚁群算法进行优化,优化的结果与实际情形具有良好的一致性,说明了改进算法的有效性和实用性。Direction guiding is utilized in the initial pheromone avoiding ant colony in the initial stage to blindly random search and to waste more time. Moreover,a dynamic factor （hyperbolic tangent function） is invited in the global renewal process to update adaptively the pheromone concentration on the optimal path, in which way the possibility of obtaining the global optimal solution is increased. Then two examples are optimized with the improved algorithm, and the optimization results are in step with the actual,illustrating the effective- ness and practicability of the improved algorithm.

关 键 词：蚁群算法 最短路径 方向引导 动态因子:旅行商问题 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]