一种新的基于logistic混沌映像的自适应混沌蚁群优化算法求解动态车辆路径问题

New ant colony optimization algorithm based on logistic chaotic image to resolve VRP problem

针对车辆路径问题(VRP),提出基于logistic函数的自适应混沌蚁群优化算法。利用混沌运动的遍历性、随机性和规律性特点,把具有强局部搜索能力的logistic映像融入到蚁群算法局部信息素更新中。屏蔽logis-tic映像断点区间,克服蚁群算法搜索时间过长、易于停滞的现象,提高算法准确度。选用VRP标准库实例进行的仿真实验表明,新算法能准确找到已知最优解,与其他算法的比较实验证明了该算法的有效性。

For vehicle routing problem（VRP）,this paper proposed a new ant colony optimization algorithm CACO（ACO with chaos image）.Put a strong local search ability chaos function（logistic）into the local pheromone update of ant colony algorithm.Made use of the ergodicity feature,randomness feature and regularity feature of chaotic motion to resolve the ASO easy-to-stagnation phenomenon,improved the algorithm veracity.Selected the standard VRP library for simulation tests to resolve the VRP problem,the new algorithm can find the optimal solution that is known.Compared with other algorithms,it proves the effectiveness of the new algorithm.