复杂环境路径规划的改进蚁群算法

摘要

为了克服传统蚁群算法在路径规划中容易出现早熟收敛、陷入局部最优、算法运行慢等缺点,提高算法在路径规划中的性能,提出了改进蚁群算法的距离启发因子、权值系数动态调整的方法,并且利用模拟退火算法的思想,改进蚁群算法的信息素挥发系数.该算法通过这种改进可以避免出现早熟收敛和陷入局部最优,且可以提高算法的运行速度.利用改进的蚁群算法进行路径规划仿真,仿真结果表明,该算法是一种有效的路径规划算法.%In order to overcome the shortcomings, such as premature convergence, in local optimum and running slowly, and improve performance of the traditional ant colony algorithm, a way for improving distance heuristic factor and the parameters of the dynamic adjustment method is presented using simulated annealing algorithm to improve the pheromone evaporation coefficient. The traditional ant colony algorithm can avoid premature convergence, in local optimum, and running slowly by this algorithm. Using the improved ant colony algorithm for simulation path planning, the simulation results show that the algorithm is an effective algorithm for path planning.