Parallel Optimization of Routing and Improvement of the Energy Consumption of an Electric Vehicle

This study tried to optimize the energy consumption of an electric vehicle on a path where there are several available paths. This has been carried out based on metaheuristic methods or more precisely on ant colony algorithms. The used algorithm in this study was implemented in python language or what is called the numpy and matplolib libraries. At first, a schema that contains several paths of different lengths and energy values was proposed. Results demonstrated that the optimal path that can be traveled by the vehicle has two main constraints. The first one concerns having energy consumption although the distance is optimized. The second one has to do with parallelizing the program using the joblib library under python. In general, this provided better results from the point of view of the execution time.