基于改进型蚁群算法的最优路径问题求解

如何高效的向用户提供最优路径是蚁群算法大规模应用于导航系统的关键问题,针对现有最优路径问题研究中蚁群算法收敛速度慢及容易发生停滞的缺点,利用A*算法的启发式信息改进蚁群算法的路径选择策略,加快算法收敛速度.同时引入遗传算法的双种群策略和蚁群系统信息素更新策略,增加全局搜索能力,避免算法出现停滞现象.仿真实验结果表明,该改进算法具有较好的稳定性和全局优化性,且收敛速度较快.

Solving Optimal Path Problem Based on Improved Ant Colony Algorithm

Efficient optimal path is the key issue of the Ant Colony Algorithm used in road traffic navigation system. Aiming at the problem of slow convergence and stagnation phenomenon of Ant Colony Algorithm, this paper introduce path selection strategy which is based on the heuristic factor of A^＊ algorithm to speed up the convergence. Meanwhile the Double-population strategy of the Genetic algorithm and the pheromone update rules of the Ant Colony System （ACS） are introduced in the algorithm, which avoid stagnation of the algorithm of the algorithm and speed up convergence. The results of the simulation experiment show that the improved algorithm has good stability, global optimization and fast convergence.