ACO-Driven Optimization: Analysis of Constraint Implementation in the Electric Vehicle Charge Optimization Problem

This paper presents a novel Ant Colony Optimization (ACO) approach to optimize electric vehicle (EV) charging schedules, specifically focusing on minimizing tardiness. Addressing real-world constraints such as power limitations and load balancing, the proposed ACO algorithm effectively explores the solution space. Inspired by ant foraging behaviour, the method strategically utilizes pheromone trails to guide the optimization process. Validation with actual EV charging data underscores the algorithm's performance in minimizing tardiness. The study focuses on the significant impact constraints have on the optimization problem, shedding light on their role in shaping efficient charging schedules. It studies the difference outcomes of implementing the same constraints in different ways. This research contributes to the elaboration and development of new and efficient solutions that will help promote the adoption of electric vehicles. Key words. Electric Vehicles, Ant Colony Optimization, Constraint Implementation, Tardiness Optimization.