蚁群算法的分析及其改进研究

蚁群优化算法（Ant Colony Optimization ACO）是一种新颖的仿生进化类算法，适用于求解各种复杂组合优化问题。当前该研究方法尚处于研究的初级阶段，本文针对传统的蚁群算法容易出现早熟和停滞现象，提出了一种新的自适应蚂蚁算法，对传统的蚁群算法中的信息素参数进行动态的自适应调整，并选取几个典型TSP问题进行实验，结果表明改进蚁群算法具有更好的搜索全局最优解的能力以及更好的稳定性和收敛性。

The Analysis of Ant Colony Algorithm and Its Advanced Research

Ant Colony Optimization （ACO） is a novel bionic evolutionary algorithm for solving complex combinatorial optimization problems.This research approach lies at initial stage at present,and a new adaptive ant algorithm is proposed for the traditional ant algorithm easily appears precocious and stagnation behavior phenomenon in this paper.And the traditional parameter of pheromone of ant colony algorithm is self-adaptive adjusted.Selecting a number of typical TSP problems to experiment,the results are indicated that the new adaptive ant colony algorithm has a better ability to search the global optimal solution and have better stability and astringency.