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式算法,算法中参数的设置一直是依靠经验和试验来确定的,造成试验工作量大而且收敛速度慢;研究中提出了一种基于自适应调整信息素的改进蚁群算法,从路径的实际信息出发,动态地分配信息素,从而使算法能较快地收敛到最优解;通过仿真试验结果表明:改进的蚁群算法在收敛速度和收敛精度方面相对于原算法都具有较好的改进效果.
Ant colony algorithm （ACA） is a new heuristic algorithm, which is developed through simulating the process of ants searching for food ; the parameters of the algorithm is usually determined by experiences and experiments,which leads to heavy work and slow convergence. In this paper, a developed algorithm is put forward, this ant colony algorithm based on automatically can adaptively adjust the information on route, start from the factual phenomena, best solution. The simulation dynamic distribution of the information, and make the solution converge to the proves that this algorithm is effective.
出处
《长沙交通学院学报》
2007年第2期32-35,共4页
Journal of Changsha Communications University
基金
交通部应用基础项目(200431982515)
关键词
蚁群算法
TSP
路径均值
ant colony algorithm
TSP
route average
