Hybrid PSO-JAYA Algorithm for Solving the Multi-Objective Electric Vehicle Routing Problem (EVRP)

作者：Tejaswini Patil, S. Mane · 发表于：2025 Global Conference on Information Technology and Communication Networks (GITCON) · 年份：2025 · DOI：10.1109/GITCON65266.2025.11377983

The increasing adoption of electric vehicles (EVs) in logistics calls for improved routing systems that minimize energy consumption, operational costs, and fleet size. This rather complex multi-objective optimization problem relates to the Electric Vehicle Routing Problem (EVRP) and involves constraints placed by battery limitations, availability of charging stations, and associated time windows for deliveries. This paper proposes a hybrid optimization approach that employs Particle Swarm Optimization (PSO) and Jaya Algorithm for solving the EVRP. The area of the hybrid model benefits from global exploration strength of PSO while at the same time using the Jaya Algorithm quality of not using any parameters in learning. The evaluation on standard benchmark datasets proves that the Hybrid PSO-Jaya Algorithm closely outperforms the individual PSO and Jaya methods with respect to energy savings, operational cost reduction, and optimal fleet use. The hybrid algorithm achieves much better convergence toward the great optimum solutions because of the equilibrium maintained between exploration and exploitation within the search space. The experimentation results further analyzed using the Inverted Generational Distance (IGD) metric shows definitely that it gets to prove the hybrid approach as superior over the current available solutions. The works contribute to developing sustainable logistics as it provides a reliable and parameter-free optimization technique for managing EVs.