基于改进初始信息素和公共路径的蚁群算法

摘要

针对基本蚁群算法收敛速度慢、容易陷入局部最优等问题,提出了一种基于对初始信息素的分布进行优化以及加强对较优解的公共路径进行利用的改进蚁群算法.改进后的算法加强了蚂蚁从其所在城市到下一步最大可选城市之间的路径上的初始信息素浓度,增强了算法对较长子路径的探索能力.对较优解的公共路径的利用则提高了改进算法的收敛速度和搜索精度.通过求解不同规模TSP(旅行商问题)并与ACS(蚁群系统)算法的对比,证明了改进蚁群算法的有效性.%Aiming at the problems of low convergence rate and local optimization of basic ant colony optimization, a new kind of improved ant colony optimization algorithm is proposed. It is based on optimizing the distribution of initial pheromone and enhancing the influence of common paths of excellent solutions.The ability to explore long paths of the improved ant colony optimization algorithm is strengthened by enhancing the pheromone concentration, when ants travel from one city to another city that belongs to the furthest scope. At the same time by strengthening the use to common paths of excellent solutions, it can greatly improve the convergence speed and the searching precision of the improved algorithm. The simulation result of solving different traveling salesman problems (TSP) by the improved algorithm and the ant colony system(ACS) algorithm shows the validity of the improved algorithm .