基于灰狼优化器改进蚁群算法的物流配送路径优化算法

Logistics Distribution Route Optimization Algorithm Based on Improved Ant Colony Algorithm of Gray Wolf Optimizer

摘要: 蚁群算法在求解多任务物流配送路径优化中，存在参数设定缺乏标准及不同情境下参数设定各不相同等问题，本文提出一种基于灰狼优化器的改进的蚁群算法的路径优化算法。针对传统蚁群算法参数设定不确定等问题，通过引入灰狼优化器，借助灰狼优化器进行全局搜索的特点以及渐进式搜索的特性，找到蚁群的最优参数，从而自动获取最优参数，解决蚁群算法的参数配置问题。最后，将改进后的算法应用于多任务物流配送路径优化中。实验结果表明，提出的算法能够自动得出更优的参数，算法具有良好的求解精度和稳健的鲁棒性。

Abstract: In solving multi task logistics distribution route optimization, ant colony algorithm has some problems, such as lack of standard parameter setting and different parameter setting in different situations. This paper proposes an improved ant colony algorithm based on gray wolf optimizer. In view of the uncertain parameter setting of traditional ant colony algorithm, the gray wolf optimizer is introduced to find the optimal parameters of ant colony by virtue of the global search and progressive search characteristics of gray wolf optimizer, so as to automatically obtain the optimal parameters and solve the parameter configuration problem of ant colony algorithm. Finally, the improved algorithm is applied to the multi task logistics distribution path optimization. Experimental results show that the proposed algorithm can automatically get better parameters, and the algorithm has good accuracy and robustness.