摘要
蚁群算法具有较强的发现较好解的能力,但同时也存在一些缺点,如容易出现停滞现象、收敛速度慢等.将遗传算法和蚁群算法结合起来,在蚁群算法的每一次迭代中,根据信息量选择解分量的初值,使用变异操作来确定解的值.通过实例与其他优化方法的结果进行比较.结果表明,该算法有较好的收敛速度及稳定性.
Ant colony algorithm possesses powerful ability in searching better solutions coexisting with the disadvantages such as easily immersing into stagnation ,slow convergence speed and so on. Ant colony algorithm is combined with genetic algorithm. In each iteration of ant colony algorithm ,the first step is to choose initial values of components by adopting the trail information ,and then determine the solution by cross and mutation operations. After comparing with other optimization algorithms, it shows that this algorithm has better stability and higher convergence speed.
出处
《武汉理工大学学报（交通科学与工程版）》
2006年第2期306-309,共4页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
国家自然科学基金项目资助(批准号:60272040)
关键词
物流配送
蚁群算法
遗传算法
路径优化
physical distribution
ant colony system
genetic algorithm
the optimization of the routing problem
