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This requires estimating an optimum route under various real\u2010world complexities, such as non\u2010uniform 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\u2010exploits 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\u2010optimal 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\u2010world 3D\u2010route 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\u2010, energy\u2010, and transportation\u2010cost with safe and smooth UAV auto\u2010navigation for developing drone doctors.<\/jats:p>","DOI":"10.1111\/exsy.13224","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T23:57:17Z","timestamp":1672271837000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Synergetic fusion of Reinforcement Learning, Grey Wolf, and Archimedes optimization algorithms for efficient health emergency response via unmanned aerial vehicle"],"prefix":"10.1111","volume":"43","author":[{"given":"Himanshu","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar  Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Sreelakshmy","sequence":"additional","affiliation":[{"name":"Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar  Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7421-295X","authenticated-orcid":false,"given":"Om Prakash","family":"Verma","sequence":"additional","affiliation":[{"name":"Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar  Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarun Kumar","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Shobhit Institute of Engineering and Technology (Deemed\u2010to\u2010be University)  Meerut Uttar Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang Wook","family":"Ahn","sequence":"additional","affiliation":[{"name":"AI Graduate School Gwangju Institute of Science and Technology  Gwangju South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kapil Kumar","family":"Goyal","sequence":"additional","affiliation":[{"name":"Department of Industrial and Production Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar  Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"e_1_2_10_2_1","volume-title":"Proceedings of the IEEE congress on evolutionary computation (CEC)","author":"Awad N. 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