Optimizing Hydrogen Refueling Station Recommendations: A Comparative Analysis Between Genetic Algorithm and Particle Swarm Optimization

One of the key advantages of hydrogen vehicles (HVs) is their eco-friendly and energy-efficient nature. However, a significant challenge arises in identifying suitable hydrogen refueling stations (HRSs). This issue is inherently complex and dynamic, influenced by factors such as station location, hydrogen demand, and user requirements. In this paper, we propose a novel approach to determine the optimal HRS for HV users. We identify the optimal the optimal HRS for a specific vehicle, considering multiple criteria. We address this problem by employing the Pickup and Delivery Problem framework, solved through both the genetic algorithm and particle swarm optimization techniques. To ensure the efficiency of our solution, we also optimize the route between the identified optimal station and the vehicle’s current location using the A-Star algorithm, which finds the shortest possible path. All proposed algorithms are tested on a real dataset to evaluate their performance in terms of accuracy and speed. We assume that the proposed method provides better results and is capable of adapting to user preferences and needs.