基于聚类的蚁群优化算法解决动态定位路径问题

nt colony algorithm can resolve dynamic optimization problems due to its robustness and adaptation. The aim of such algorithms in dynamic environments is no longer to find an optimal solution but to trail it over time. In this paper, a clustering ant colony algorithm with three immigrants schemes (K-ACS) is proposed to address the dynamic location routing problem (LRP). The LRP is divided into two parts constituted by a location allocation problem (LAP) and a vehicles routing problem (VRP) in dynamic environments. To deal with the LAP, a K-means clustering algorithm is used to tackle the location of depots and surrounding cities in each class. Then the ant colony algorithm with three immigrants including random immigrants, elitism-based immigrants and memory-based immigrants is utilized to handle the VRP in dynamic environments consisting of random and cyclic traffic factors. A comparative study is carried out to assess the effect of the utilization of clustering. Experimental results based on different scales of LRP instances demonstrate that the clustering algorithm can significantly improve the performance of K-ACS in terms of the qualities and robustness of solutions. Furthermore, K-ACS show promising performance in solving the LRP in two different dynamic environments, suggesting that the proposed algorithm may lead to a new technique for tracking the environmental changes by utilizing its clustering and evolutionary characteristics.