Optimizing Police Patrol Strategies in Real-World Scenarios: A Modified PPS-MOEA/D Approach for Constrained Multi-Objective Optimization

This study addresses the realistic constrained multi-objective optimization problem of police patrols by constructing a mathematical model tailored to the actual operational context of police patrols in China. To solve this problem, a modified PPS-MOEA/D algorithm is proposed and its performance is systematically evaluated against several state-of-the-art Constrained Multi-Objective Evolutionary Algorithms (CMOEAs). The results demonstrate the superiority of the proposed approach in terms of the solution quality and computational efficiency. Furthermore, the optimal solution set is discussed and visualized on a map, providing decision makers with practical and actionable insights that align with real-world patrol requirements. This research not only advances the theoretical framework for police patrol optimization, but also offers a practical tool for enhancing the effectiveness and efficiency of law enforcement operations in urban environments.