Synergetic fusion of Reinforcement Learning, Grey Wolf, and Archimedes optimization algorithms for efficient health emergency response via unmanned aerial vehicle

Abstract

Owing to the recent technological innovations, unmanned aerial vehicles (UAVs) are progressively employed in various civil and military applications, including healthcare. This requires estimating an optimum route under various real-world complexities, such as non-uniform obstacles. However, most of the reported work considers only uniform obstacles as an object, which limit their practical applicability. Hence, Archimedes optimization algorithm (AOA) is examined to overcome this limitation. Further, it is observed that many a time, AOA over-exploits the search space, resulting in higher computational time. Therefore, the present work fuses AOA with grey wolf optimizer (GWO) to improve the convergence capability. Also, reinforcement learning (RL) is employed to intelligently switch between the exploration and exploitation phases. The efficacy of the developed algorithm is statistically analysed and validated against various metaheuristics on several benchmark functions. The simulated results verified that the developed RLGA provides optimal or near-optimal solutions more efficiently relative to other metaheuristics. Moreover, it also affirms the hypothesis that the proposed modifications significantly improve the convergence speed of AOA. Finally, the appropriateness of RLGA is tested and validated by rigorous experimentation on real-world 3D-route estimation problems for UAVs. The simulated results reveal that RLGA produces a flyable path with 51.46%, 62.06%, and 70.42% lesser cost than RLGWO, AOA, and GWO, respectively. This ensures the employability of RLGA for efficient medical assistance in minimum time-, energy-, and transportation-cost with safe and smooth UAV auto-navigation for developing drone doctors.