自适应调整信息素的蚁群算法  

AN IMPROVED ANT COLONY ALGORITHM BASED ON ADAPTIVELY=ADJUSTING PHEROMONE

作　　者：覃刚力[1] 杨家本[1] 

机构地区：[1]清华大学自动化系,北京100084

出　　处：《信息与控制》2002年第3期198-201,210,共5页Information and Control

摘　　要：蚁群算法是通过模拟蚂蚁觅食而发展出的一种新的启发算法 .基于群体的协作与学习 ,该算法已经成功地解决诸如 TSP问题等多种组合优化问题 .本文提出了一种基于自适应调整信息素的改进蚁群算法 .该算法根据人工蚂蚁所获得解的情况 ,动态地调整路径上的信息素 ,从而使得算法跳离局部最优解 .通过仿真实验获得的结果表明 ,该算法对于蚁群算法具有较好的改进效果 .Ant colony algorithm (ACA) is a new heuristic algorithm, which is successfully used to solve some NP-hard combinatorial optimization problems through simulating the process of ants searching for food. In this contribution a new ACA, which is based on adaptively adjusting the pheromone on routes according to the solutions that artificial ants have found, will be proposed. Thus it can escape from the local maximum. Simulations demonstrate that the improved algorithm can achieve better performance than basic ant colony algorithm.

关 键 词：自适应调整 信息素 蚁群算法 TSP问题 组合优化问题 

分 类 号：O224[理学—运筹学与控制论]