一种基于蚁群思想的动态路径寻优算法的实现及仿真

Implementation and Simulation of a Dynamic Path Optimization Algorithm Based on Ant Colony Thoughts

为了提高路径寻优算法的效率和实时性,本文实现了一种名为DPO-AC的基于蚁群思想的动态路径寻优算法(the ant colony algorithm with dynamic path optimization),在改进蚁群算法的基础上结合神经网络的实时预测方法和限定区域的搜索方式,解决算法在大型网络路径寻优时实时性差、收敛慢的问题。仿真实验表明DLACO算法有比较好的稳定性、收敛性和实时性。

In order to improve the efficiency and instantaneity of path optimization algorithms,a dynamic path optimi- zation algorithm named the ant colony algorithm with dynamic path optimization（DPO-AC） was proposed. On the basis of the improved ant colony algorithm and combined with neural network real-time prediction and limited area search, the proposed algorithm solved the problem of poor real-time performance and slow convergence of large net- work path optimization algorithms. Simulation results show that DL-ACO has better stability, convergence and in- stantaneity.