Multi-phase shuffled frog leaping algorithm with power law extremal optimization search for multi-depot vehicle routing problem

Abstract

In the present work, a multi-phase hybrid algorithm based on clustering is presented to solve the multi-depot vehicle routing problem (MDVRP). The proposed algorithm initially adopts the K-means algorithm to perform the clustering analyses considering the depots as the centroids of the clusters for all the customers of MDVRP. Afterward, it implements the local depth search using the Shuffled Frog Leaping Algorithm (SFLA) for every cluster, and then globally re-adjusts the solutions, i.e., rectifies the positions of all frogs by Power Law Extremal Optimization Neighborhood Search (PLEONS). In the next step, a new clustering analyses is performed to generate new clusters according to the best solution achieved by the preceding process. The improved path information is inherited to the new clusters, and the local search using SFLA for every cluster is used again. The processes continue until the convergence criterions are satisfied. The experiment results show that the proposed algorithm possesses outstanding performance to solve the MDVRP and the MDVRP with time windows.