Optimizing Vehicle Placement in the Residual Spaces of Unmarked Parking Areas: A Comparative Study of Heuristic Methods

Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle Swarm Optimization, Artificial Bee Colony, Gray Wolf Optimizer, Harris Hawks Optimizer, Phasor Particle Swarm Optimization, Multi-Population Based Differential Evolution, and the Colony-Based Search Algorithm. The experiments were conducted in two different parking areas, one designed for parallel parking and the other for perpendicular parking, under three scenarios allowing different levels of cars’ rotational flexibility. The results indicate that MDE consistently outperforms other methods in both speed and robustness, achieving the highest vehicle capacity. These findings provide a foundation for smart parking systems, enabling real-time optimization, reduced congestion, and improved urban mobility.