Cooperative search approach for UAVs via Pigeon-inspired Optimization and Markov moving targets

To solve the problem of repeated search, static targets and low efficiency in cooperative search for multi-UAVs, a method based on pigeon-inspired optimization (PIO) and Markov is proposed. Firstly, a honeycomb environmental model similar to the sensor detect region is established to reduce repeated search for the area. Secondly, Markov chain with the Gaussian distribution is used to represent dynamic movement of targets. Thirdly, the Cauchy mutation and Gaussian mutation are introduced into the map and compass operator and the landmark operator of PIO, respectively. Meanwhile, simulated annealing (SA) mechanism is exploited to reserve the worse individual, so as to effectively reduce the problem that PIO is easy to fall into local optimum. Finally, the algorithm is compared with other swarm intelligence algorithms through simulation experiments. The results show that the new method is effective and available.