Optimizing Electric Vehicle Routing: A Statistical Analysis of ACO, GA, and SA Algorithms

In the world of electric vehicle (EV) navigation, the quest for the best routing algorithms is crucial for creating efficient and sustainable transportation options. Conventional methods like Dijkstra's Algorithm show limitations in scalability and adaptability in changing conditions, which require frequent adjustments. This study supports using Ant-Colony Optimization (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA) as heuristic techniques to optimize EV routes. A comprehensive assessment of several optimization algorithms for solving the Electric Vehicle Routing Problem (EVRP) provided valuable information on their efficiency and effectiveness. This research evaluates Ant-Colony Optimization (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA) as possible approaches to optimize the Electric Vehicle Routing Problem (EVRP). ACO stands out as the top contender, showing better precision and dependability in providing the best routing options for various EVRP situations. Despite the extended computational duration, ACO stands out in pinpointing the most optimal travel distances, making it a strong option for EVRP optimization. Simulated Annealing shows respectable performance despite some variability, coming in behind the Genetic Algorithm in ranking for finding the shortest routes. ACO's effectiveness in addressing convergence problems and providing eco-friendly transportation solutions makes it the top choice for optimizing EV routing. The provided visual representation explains how ACO works by showing the most efficient routes between set map points, strategically connected to charging stations for user convenience.