Battlefield Optimization Algorithm to Optimize Electric Vehicle Charging Station Placement

This paper applies the Battlefield Optimization Algorithm (BfOA) to a mathematical model of optimization of electric vehicle charging station placement based on the population density and the charging stations' proximity to the population. Three classic metaheuristic methods, Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization, as well as three modern metaheuristic methods, War Strategy Optimization, Equilibrium Optimizer, and Komodo Mlipir Algorithm, are also implemented to validate the performance of BfOA. The evaluation is conducted in two cases with 50 and 100 charging stations to test the scalability of the algorithms. Both cases are in the Greater Bandung, Indonesia, because of the large population and the inequality in the population distribution. Experiments show that BfOA has the best-fit score of all the tested algorithms. BfOA shows an ability to balance exploration and exploitation, making it one of the most significant factors in finding the optimal solution compared to others. It also shows the scalability of performing best in both small and large cases. It is stable in solving problems with different dimensions. Besides, it also spreads charging stations objectively by placing more than one charging station in dense areas (green areas) while still supplying the rural areas.