Modified Aquila Optimization based Route Planning Model for Unmanned Aerial Vehicles Networks

Unmanned aerial vehicles (UAVs) are deliberated as a potential example of automated emergency tasks in dynamic marine environments. But the maritime transmission performances among UAVs and offshore platform becomes a crucial problem. The task planning problems of numerous UAVs are classified into two parts, route planning and task allocation problems, are different and interrelated from one another. Because of the complicated marine environments, both efficiencies of UAVs in an intelligent ocean are not acceptable. This study presents a Modified Aquila Optimization Algorithm based Route Planning Scheme (MAOA-RPS) for UAV networks. The presented MAOA-RPS technique is majorly concentrated on the detection of optimal routes for UAV data transmission. To attain this, the MAOA-RPS technique involves the incorporation of Levy flight (LF) with the traditional AOA. In addition, the optimal routes are chosen by the MAOA-RPS technique via a fitness value, which can be determined by many input parameters. For assessing the enhanced outcomes of the MAOA-RPS technique, we have performed a series of experiments. The comparison study revealed the improved performance of the MAOA-RPS technique under distinct metrics.