Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more scholars’ attention. At present, there bionic algorithms for solving problem multi-UAVs, including particle swarm optimization algorithm (PSO) differential evolution (DE). Pigeon-inspired (PIO) is a new intelligence proposed recent years. It great advantages over other convergence, robustness, accuracy, few parameters be adjusted. Aiming at shortcomings standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, ease falling into local optimum, we chaotic disturbance pigeon-inspired (CDPIO) algorithm. The improved tent map was used initialize increase diversity population. factor introduced iterative update stage generate individuals, replace individuals performance, carry out accuracy. Benchmark functions UAV model were test performance. results show that CDPIO had faster better precision, performance than PIO.