A Multi-Criteria Parking Space Proposing System based on Cheetah Optimizer Algorithm

Efficient allocation of parking spaces in urban environments remains a significant challenge due to diverse user preferences such as cost, proximity, and convenience. This paper proposes a novel intelligent parking assignment framework based on the Cheetah Optimization Algorithm (COA), a bio-inspired metaheuristic mimicking the adaptive hunting behavior of cheetahs. The method integrates user-specific criteria in a multi-stage process, first collecting system and driver data, then applying COA to optimize parking space allocation. Compared to deep reinforcement learning and other metaheuristics like Genetic Algorithm and Whale Optimization Algorithm, COA demonstrates faster convergence, and improved solution quality. The results confirm that COA is an effective and robust approach for real-time, personalized smart parking management in dynamic urban settings.