基于参数动态变化和变异的蚁群算法

针对蚁群算法存在求解速度慢、容易出现早熟和停滞现象，提出一种基于参数动态变化和变异的自适应蚁群算法(PDMACS)。将参数分为全局参数和局部参数，对参数的功能进行讨论，设计局部参数q0随蚂蚁求解质量动态变化和全局参数?随平均节点分支数自适应调整的方法提高算法全局搜索能力，并采用一种简单高效的变异算法加快收敛速度。用TSPLIB中的范例进行比较实验，结果表明，与传统算法相比，该算法的求解质量、稳定性以及收敛速度都有所提高。

Ant Colony Algorithm Based on Dynamic Change of Parameters and Mutation

To settle the contradictory between convergence speed and precocity and stagnation in ant colony algorithm,an adaptive ant colony algorithm,which is based on dynamic change of parameters and mutation,is presented by analyzing in-depth the parameters of model.The parameters are divided into global parameters and local parameters.The methods,of which the local parameter q0 changes accordingly with the change of the quality and the global parameter ρ changes accordingly with the number of average node branching,are designed,which make the global searching ability enhance remarkably.A simple and efficient mutation algorithm is adopted to accelerate convergence speed.Experimental results of TSPLIB show that the method presented in this paper has much higher quality and stability and convergence speed than that of classical ant colony algorithm.