Coverage optimization for 3D wireless sensor networks based on improved zebra optimization algorithm

A 3-dimensional wireless sensor network (3D-WSN) coverage optimization algorithm is proposed to address the issues of low coverage within 3D-WSN. In the proposed IZOA, first, the initial step involves the utilisation of Cauchy mutation, which serves to augment the global search capability. Second, the deployment of a multi-strategy pool enhances the ability to circumvent convergence towards local optima. Finally, the implementation of lens imaging backward learning enhances the optimality-seeking capability of the IZOA and accelerates convergence rates. The IZOA is employed for the optimization of 3D-WSN coverage. Simulation experiments demonstrate that IZOA yields the optimal solution and enhances the coverage rate of 3D-WSN in comparison to PSO, WOA, SSA and ZOA.