Artificial Bee Colony-Based Optimization for Public Electric Vehicle Charging Station Placement

Background: The urgency of developing Electric Vehicle Charging Stations (EVCS) infrastructure is increasing alongside the need for low-emission mobility and energy efficiency. Objective: This study aims to optimize the determination of EVCS locations using the Artificial Bee Colony (ABC) method. Methods: This method was selected for its capability to find optimal solutions through an iterative population-based approach. Simulations were conducted by limiting the maximum iterations to 1000 to evaluate the impact of iteration numbers on optimization quality. Results: The results show that the ABC method successfully identified the shortest distance from three initial locations to the optimal EVCS locations. In the second simulation, the shortest distance obtained was 0.6420 km, indicating that an increase in the number of iterations correlates directly with the quality of optimization results. Specifically, the optimal distance from the first initial location to the EVCS at Danareksa Tower was 1.7018 km using the ultra-fast charging type. From the second initial location to the EVCS at the Ministry of State-Owned Enterprises Building, the optimal distance was 0.6420 km using the fast-charging type. Meanwhile, from the third initial location to the EVCS at PLN UID Greater Jakarta, the optimal distance was 1.1787 km using the ultra-fast charging type. Conclusion: This study demonstrates that the ABC method can deliver accurate results in determining optimal EVCS locations with efficient distances. These findings are expected to support the development of more effective and integrated electric vehicle infrastructure.