Design of Rider Invasive Weed Optimization Algorithm for Unmanned Aerial Vehicle Path Planning

The unmanned aerial vehicle (UAV) is concerned with extensive attention in either military or civilian domains due to minimum cost, flexibility, smaller size, and so on. Proper and reliable path planning (PP) is fundamental to ensure security and mission success because of the gradually difficult atmosphere. The UAV only depends on its restricted sensors for obtaining partial threat data. PP is an essential technology for the autonomous flight of UAVs. The standard PP techniques take some deficiencies and restrictions in difficult and dynamic environments due to massive action and state spaces. PP for the UAV has recently developed a research hotspot and received varied attention from researchers. This study designs a Rider Invasive Weed Optimization Algorithm for the Unmanned Aerial Vehicle Path Planning (RIWO-UAVPP) technique. The presented RIWO-UAVPP technique focuses on identifying optimal routes for data transmission in the UAV network. To accomplish this, the RIWO-UAVPP technique exploits the characteristics of the RIWO algorithm, which is a hybridized version of the Rider Optimization Algorithm (ROA) and Invasive Weed Optimization (IWO) algorithm. The design of the PP process involves a Fitness Function (FF) with varying UAV parameters. The experimental output examination of the RIWO-UAVPP procedure is investigated under several measures. The experimental outcomes signified the enhanced achievement of the RIWO-UAVPP procedure over other recent procedures.