Police UAV plays an increasingly important role in improving new quality combat capability of public security organs. To solve the path planning problem of police UAV (PU3P), this paper proposed a police UAV path planning method based on an improved Particle Swarm Optimization (PSO) using AFS and HJS. Firstly, a specific Adaptive Factor Strategy (AFS) was introduced into original PSO in order to strengthen overall exploration capability while Half Jumping Strategy (HJS) was adopted to enhance it local exploitation level, thus an improved PSO(AFS-HJS-PSO) using AFS and HJS was proposed. Secondly, a police UAV path planning method (AFS-HJS-PSO-PU3P) was proposed by utilizing AFS-HJS-PSO to optimize police UAV path planning fitness function (UPPF) abstracted from PU3P,devoted to get the global best solution for police UAV path planning. Finally, comparative experiments among PSO, Generic Algorithm (GA),Simulated Annealing Algorithm (SAA), a Nonlinear Programming Solver: FMINCON, AFS-PSO and AFS-HJS-PSO were independently conducted 30 times on 20 classical benchmark functions as well as the ones among PSO-PU3P,GA-PU3P,SAA-PU3P,AFS-PSO-PU3P and AFS-HJS-PSO-PU3P were conducted 30 times on UPPF, theoretical research and experimental results indicate that new proposed AFS-HJS-PSO is superior to PSO, GA, SAA, FMINCON in optimization accuracy, convergence speed and stability as well as AFS-HJS-PSO-PU3P can better solve PU3P than other compared methods.
Wang et al. (Fri,) studied this question.