The Vehicle Routing Problem (VRP) is one of the most important problems in supply chain management because the optimal allocation of vehicles has a significant impact on reducing costs. VRP is in the class of NP-hard problems and exact algorithms cannot find the best so More
The Vehicle Routing Problem (VRP) is one of the most important problems in supply chain management because the optimal allocation of vehicles has a significant impact on reducing costs. VRP is in the class of NP-hard problems and exact algorithms cannot find the best solution in an acceptable time. Hence, meta-heuristic algorithms can be employed to solve it. Shuffled Frog Leaping Algorithm (SFLA) is one of the meta-heuristic algorithms, which is efficient, but in some cases, its population diversity rapidly reduces, and the algorithm falls in local optima. In this study, an Individual-Oriented Shuffled Frog Leaping Algorithm (IO-SFLA) is proposed to enhance the exploration and exploitation of SFLA by exchanging the global and local information. Several VRPs in different dimensions are applied to evaluate the performance of IO-SFLA. The efficiency of IO-SFLA is compared with several improved shuffled frog leaping algorithms, Simulated Annealing (SA) and Genetic Algorithm (GA). The results show that IO-SFLA provides significant results compared with the other competitor algorithms. IO-SFLA achieves an average of 1130.442 for the best path cost. The next rank belongs to SA with an average of 1228.725. Other compared algorithms are in the lower ranks with high differences in results.
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