Public Security Patrol Path Planning Recommendation Method based on Wolf-Pack Optimization Algorithm using DAF and BRS

The public security patrol path planning plays an important role in public security work, however, existing public security patrol path planning has varying degrees of shortcomings. To address these shortcomings, this paper proposes a public security patrol path planning recommendation method based on an improved wolf-pack optimization algorithm (S3PRM-DAF-BRS-CWOA). Firstly, an optimization objective function regarding the public security patrol path planning(S3P-Function) was abstracted based on the actual situation; Secondly, this paper proposed an improved wolf-pack optimization algorithm named DAF-BRS-CWOA using Dynamic-Adjustment-Factor(DAF) and Balanced-Raid-Strategy(BRS), and DAF devoted to adjust the overall wolf-pack running strategy by dynamically adjusting the number of airdropped wolves during the stage of Summon-Raid while BRS with symmetric property was to improve both the algorithm's global exploration as well as the local development capabilities by increasing the number of checking locations, that means not only checking the reverse position of the current wolf, but also the positions generated according to certain rules between the reverse position of the current wolf and the current optimal wolf during the stage of Summon-Raid; Finally, DAF-BRS-CWOA was adopted to optimize S3P-Function, forming a public security patrol path planning recommendation method based on DAF-BRS-CWOA (S3PRM-DAF-BRS-CWOA). Comparative and numerical experiments with four similar swarm intelligence optimization algorithms (PSO, GA, WDX-WPOA and DAF-BRS-CWOA) were conducted on 20 public datasets as well as the proposed S3P-Function, and the experimental results demonstrated that S3PRM-DAF-BRS-CWOA has superior performance as same as DAF-BRS-CWOA.