Improved Antlion Algorithm for Electric Vehicle Charging Station Placement

Abstract

Finding the most suitable sites for charging stations (CSs) presents the main challenge to expand the usage of electric vehicle (EV). For this reason, we propose a new model to solve the problem of CSs placement by taking into consideration several parameters. In this work, the travel cost, maintenance, and installation charges of several types of stations are the main variables for calculating the objective function. In addition, we take into account two important constraints: budget limitation and charging station capacity. This problem is described as an NP-hard problem, hence the need to use an optimization method based on meta-heuristics that have proven their effectiveness before.For this purpose, we propose an Improved Antlion Algorithm (IALO) combined with a search heuristic. To assess this approach, we compare it with the most commonly used and recent optimization algorithms, in particular the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and Atom Search Optimization (ASO). Experimental results show that improved antlion algorithm provide better solutions than algorithms mentioned above.