蚁群算法是一种新颖的求解复杂优化组合问题的模拟进化算法,它具有典型的群体智能的特性,该算法的主要特点是正反馈、分布式计算、鲁棒性和并行性等,在许多领域都得到了成功应用;文章首先简述了蚁群的觅食行为及蚂蚁的信息系统,其次介绍了人工蚁群算法的基本原理及其主要特点,介绍了蚁群算法的模型和算法框图,并用蚁群算法对旅行商问题(Traveling Salesman Problem,TSP)进行了matlab仿真实现(设置蚂蚁个数31,启发式因子为1,期望启发因子为5,信息素的挥发系数为0.1,最大迭代次数为200,信息素强度系数为100,城市个数为31,用蚁群算法得出了31个城市的TSP最短路径和收敛曲线);最后介绍了近年来蚁群算法及其在组合优化中的应用研究成果,并对蚁群算法未来的发展方向进行了探讨.%Ant colony algorithm is a novel simulating evolution algorithm with typical swarm intelligence feature and is used to solve some complicated NP hard combinatorial optimization problems. This algorithm is applied to a lot of fields triumphantly, for its several characteristics, such as positive feedback, distributed computing, robustness and parallelism. This paper outlines the ant coiony's foraging behavior and information system. Then the basic principle and the main characteristics of artificial ant colony algorithm are presented. The ant colony algorithm model and algorithm block diagram are introduced too. At the same time, ant colony algorithm matlab simulation for TSP problem has been done. Finally, the recent research achievement of ant colony algorithm and its application on combinatorial optimization problems is introduced and the future development of ant colony algorithm is discussed in article.
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