A Police Booth Planning Method Based on Wolf Pack Optimization Algorithm Using AAF and DGSS

Efficient police booth deployment is vital for optimizing law enforcement and maintaining public safety. This paper tackles two key challenges in current solutions: insufficient coverage and redundant overlaps. First, this article introduces the Simultaneous Optimization for Max Coverage and Min Overlap model (SOOM-MCMO), which formalizes the dual objectives into a unified optimization framework. Second, to address limitations of traditional wolf pack algorithm, this article presents the Adaptive-Approaching Framework with Dynamic-Grid-Siege Wolf Pack Algorithm (AAF-DGS-WPOA). This technique dynamically adjusts searcher populations using spatial symmetry and hybrid optimization strategies, thereby enhancing coverage precision and reducing computational costs. Our proposed method (AAF-DGS-WPOA-based Police Booth Planning Method, PBPM-AAFDGS-WPOA) was evaluated on 20 public datasets as well as SOOM-MCMO. Results showed 15–30% coverage improvement and a 16.65% runtime reduction versus popular benchmarks like PSO, GA, and WDX_WPOA. The improved wolf pack algorithm also outperformed traditional approaches in coverage sufficiency, resource efficiency, and system responsiveness. This work advances practical methods for police booth planning, achieving higher social security outputs with lower resource investment.