Research on Ad Hoc network Coverage Optimization for Unmanned Aerial Vehicles based on improved Zebra Optimization Algorithm

Aiming at the problem of low network coverage due to uneven node deployment in UAV ad hoc networks, the multi-strategy improved Zebra Optimization Algorithm Network Coverage (MZOC) is proposed for UAV ad hoc networks to improve the shortcomings of insufficient optimization accuracy and easy-to-fall into local optimum. Firstly, the Circle chaotic mapping is introduced in the initial phase to generate chaotic sequences to obtain a more uniform search space; secondly, the nonlinear weights and golden sine strategy are introduced in the foraging phase to optimize and update the zebra positions in the foraging phase; and lastly, the adaptive t-distribution perturbation strategy is introduced in the defense phase to improve the algorithm’s performance of searching for the optimum. A comparison of simulation experiments shows that the coverage rate of the MZOC algorithm proposed in this paper is ${3. 3 5 3} \%, {1 5. 2 3 7 \%}$, and ${3. 1 3 7 \%}$ higher than that of the ZOA, EO algorithm, and GWO algorithm, respectively, which improves the phenomenon of uneven distribution of the nodes and improves the utilization rate of the nodes.