{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:42:36Z","timestamp":1779295356553,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["723B2002"],"award-info":[{"award-number":["723B2002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Innovation Team of Shaanxi Province","award":["2023-CX-TD-07"],"award-info":[{"award-number":["2023-CX-TD-07"]}]},{"name":"Natural Science Foundation Project of Hunan Province","award":["2024JJ5109"],"award-info":[{"award-number":["2024JJ5109"]}]},{"name":"Key R&D Program Projects in Shaanxi Province","award":["2024GH-ZDXM-48"],"award-info":[{"award-number":["2024GH-ZDXM-48"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s12293-024-00420-8","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:02:26Z","timestamp":1723460546000},"page":"373-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A reinforcement learning-based evolutionary algorithm for the unmanned aerial vehicles maritime search and rescue path planning problem considering multiple rescue centers"],"prefix":"10.1007","volume":"16","author":[{"given":"Haowen","family":"Zhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingbo","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjie","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lining","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengyun","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"issue":"7","key":"420_CR1","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.3390\/su11072084","volume":"11","author":"OTOTED Agbissoh","year":"2019","unstructured":"Agbissoh OTOTED, Li B, Ai B, Gao S, Xu J, Chen X, Lv G (2019) A decision-making algorithm for maritime search and rescue plan. Sustainability 11(7):2084","journal-title":"Sustainability"},{"key":"420_CR2","doi-asserted-by":"publisher","first-page":"110797","DOI":"10.1016\/j.oceaneng.2022.110797","volume":"248","author":"X Zhou","year":"2022","unstructured":"Zhou X (2022) A comprehensive framework for assessing navigation risk and deploying maritime emergency resources in the south china sea. Ocean Eng 248:110797","journal-title":"Ocean Eng"},{"key":"420_CR3","doi-asserted-by":"crossref","unstructured":"Lee S, Morrison JR (2015) Decision support scheduling for maritime search and rescue planning with a system of uavs and fuel service stations. In: 2015 International conference on unmanned aircraft systems (ICUAS). IEEE, pp. 1168\u20131177","DOI":"10.1109\/ICUAS.2015.7152409"},{"key":"420_CR4","doi-asserted-by":"publisher","first-page":"110098","DOI":"10.1016\/j.oceaneng.2021.110098","volume":"241","author":"B Ai","year":"2021","unstructured":"Ai B, Jia M, Xu H, Xu J, Wen Z, Li B, Zhang D (2021) Coverage path planning for maritime search and rescue using reinforcement learning. Ocean Eng 241:110098","journal-title":"Ocean Eng"},{"key":"420_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2024.3369485","author":"Z Wang","year":"2024","unstructured":"Wang Z, Gao W, Li G, Wang Z, Gong M (2024) Path planning for unmanned aerial vehicle via off-policy reinforcement learning with enhanced exploration. IEEE Trans Emerg Topics Comput Intell. https:\/\/doi.org\/10.1109\/TETCI.2024.3369485","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"issue":"22","key":"420_CR6","doi-asserted-by":"publisher","first-page":"4964","DOI":"10.3390\/app9224964","volume":"9","author":"Yue Guan Wang","year":"2019","unstructured":"Yue Guan Wang (2019) A novel searching method using reinforcement learning scheme for multi-uavs in unknown environments. Appl Sci 9(22):4964","journal-title":"Appl Sci"},{"key":"420_CR7","doi-asserted-by":"publisher","first-page":"116921","DOI":"10.1016\/j.oceaneng.2024.116921","volume":"296","author":"L Zhao","year":"2024","unstructured":"Zhao L, Bai Y, Paik JK (2024) Optimal coverage path planning for usv-assisted coastal bathymetric survey: models, solutions, and lake trials. Ocean Eng 296:116921","journal-title":"Ocean Eng"},{"key":"420_CR8","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1016\/j.ejor.2021.09.008","volume":"300","author":"NA Kyriakakis","year":"2022","unstructured":"Kyriakakis NA, Marinaki M, Matsatsinis N, Marinakis Y (2022) A cumulative unmanned aerial vehicle routing problem approach for humanitarian coverage path planning. Eur J Oper Res 300:992\u20131004","journal-title":"Eur J Oper Res"},{"issue":"4","key":"420_CR9","doi-asserted-by":"publisher","first-page":"781","DOI":"10.3390\/jmse11040781","volume":"11","author":"Y Ma","year":"2023","unstructured":"Ma Y, Li B, Huang W, Fan Q (2023) An improved NSGA-II based on multi-task optimization for multi-uav maritime search and rescue under severe weather. J Marine Sci Eng 11(4):781. https:\/\/doi.org\/10.3390\/jmse11040781","journal-title":"J Marine Sci Eng"},{"key":"420_CR10","doi-asserted-by":"publisher","first-page":"112178","DOI":"10.1016\/j.oceaneng.2022.112178","volume":"261","author":"Q Ma","year":"2022","unstructured":"Ma Q, Zhang D, Wan C, Zhang J, Lyu N (2022) Multi-objective emergency resources allocation optimization for maritime search and rescue considering accident black-spots. Ocean Eng 261:112178. https:\/\/doi.org\/10.1016\/j.oceaneng.2022.112178","journal-title":"Ocean Eng"},{"key":"420_CR11","doi-asserted-by":"publisher","first-page":"113444","DOI":"10.1016\/j.oceaneng.2022.113444","volume":"270","author":"J Wu","year":"2023","unstructured":"Wu J, Cheng L, Chu S (2023) Modeling the leeway drift characteristics of persons-in-water at a sea-area scale in the seas of China. Ocean Eng 270:113444","journal-title":"Ocean Eng"},{"key":"420_CR12","doi-asserted-by":"crossref","unstructured":"Koopman BO (1957) The theory of search: Iii. the optimum distribution of searching effort. Operations research 5(5), 613\u2013626. INFORMS","DOI":"10.1287\/opre.5.5.613"},{"key":"420_CR13","doi-asserted-by":"crossref","unstructured":"Karakaya M (2014) Uav route planning for maximum target coverage. arXiv preprint arXiv:1403.2906","DOI":"10.5121\/cseij.2014.4103"},{"key":"420_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/app13042169","author":"L Yang","year":"2023","unstructured":"Yang L, Yin R, Xue Y, Tian Y, Liu H (2023) A time-domain planning method for surface rescue process of amphibious aircraft for medium\/distant maritime rescue. Appl Sci-basel. https:\/\/doi.org\/10.3390\/app13042169","journal-title":"Appl Sci-basel"},{"key":"420_CR15","doi-asserted-by":"crossref","unstructured":"Theile M, Bayerlein H, Nai R, Gesbert D, Caccamo M (2020) UAV coverage path planning under varying power constraints using deep reinforcement learning. In: 2020 IEEE RSJ International conference on intelligent robots and systems (IROS). IEEE, pp. 1444\u20131449","DOI":"10.1109\/IROS45743.2020.9340934"},{"key":"420_CR16","doi-asserted-by":"crossref","unstructured":"Li B, Patankar S, Moridian B, Mahmoudian N (2018) Planning large-scale search and rescue using team of uavs and charging stations. In: 2018 IEEE International symposium on safety, security, and rescue robotics (SSRR). IEEE, pp. 1\u20138","DOI":"10.1109\/SSRR.2018.8468640"},{"key":"420_CR17","doi-asserted-by":"crossref","unstructured":"Li L, Gu Q, Liu L (2020) Research on path planning algorithm for multi-uav maritime targets search based on genetic algorithm. In: 2020 IEEE international conference on information technology, big data and artificial intelligence (ICIBA). IEEE, vol. 1, pp. 840\u2013843","DOI":"10.1109\/ICIBA50161.2020.9277470"},{"issue":"18","key":"420_CR18","doi-asserted-by":"publisher","first-page":"17440","DOI":"10.1109\/JIOT.2022.3155697","volume":"9","author":"M Xi","year":"2022","unstructured":"Xi M, Yang J, Wen J, Liu H, Li Y, Song HH (2022) Comprehensive ocean information-enabled AUV path planning via reinforcement learning. IEEE Internet Things J 9(18):17440\u201317451","journal-title":"IEEE Internet Things J"},{"key":"420_CR19","unstructured":"Jonnarth A, Zhao J, Felsberg M (2023) End-to-end reinforcement learning for online coverage path planning in unknown environments. arXiv preprint arXiv:2306.16978"},{"key":"420_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2023.3327792","author":"R Li","year":"2023","unstructured":"Li R, Gong W, Wang L, Lu C, Pan Z, Zhuang X (2023) Double dqn-based coevolution for green distributed heterogeneous hybrid flowshop scheduling with multiple priorities of jobs. IEEE Trans Autom Sci Eng. https:\/\/doi.org\/10.1109\/TASE.2023.3327792","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"420_CR21","doi-asserted-by":"publisher","first-page":"101236101236","DOI":"10.1016\/j.swevo.2023.101236","volume":"77","author":"Y Song","year":"2023","unstructured":"Song Y, Wei L, Yang Q, Wu J, Xing L, Chen Y (2023) Rl-ga: a reinforcement learning-based genetic algorithm for electromagnetic detection satellite scheduling problem. Swarm Evol Comput 77:101236101236","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"420_CR22","first-page":"2548","volume":"24","author":"S Rani","year":"2022","unstructured":"Rani S, Babbar H, Kaur P, Alshehri MD, Shah SH (2022) An optimized approach of dynamic target nodes in wireless sensor network using bio inspired algorithms for maritime rescue. IEEE Trans Intell Transp Syst 24(2):2548\u20132555","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"420_CR23","first-page":"1","volume":"10","author":"Y Zhou","year":"2024","unstructured":"Zhou Y, Kong L, Yan L, Liu Y, Wang H (2024) A memetic algorithm for a real-world dynamic pickup and delivery problem. Memetic Comput 10:1\u201315","journal-title":"Memetic Comput"},{"issue":"4","key":"420_CR24","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/s12293-022-00377-6","volume":"14","author":"L Chen","year":"2022","unstructured":"Chen L, Liu H, Liu H-L, Gu F (2022) A bi-level transformation based evolutionary algorithm framework for equality constrained optimization. Memetic Comput 14(4):423\u2013432","journal-title":"Memetic Comput"},{"issue":"3","key":"420_CR25","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s12293-022-00365-w","volume":"14","author":"G Palubeckis","year":"2022","unstructured":"Palubeckis G (2022) Metaheuristic approaches for ratio cut and normalized cut graph partitioning. Memetic Comput 14(3):253\u2013285","journal-title":"Memetic Comput"},{"key":"420_CR26","doi-asserted-by":"publisher","first-page":"116403","DOI":"10.1016\/j.oceaneng.2023.116403","volume":"291","author":"J Wu","year":"2024","unstructured":"Wu J, Cheng L, Chu S, Song Y (2024) An autonomous coverage path planning algorithm for maritime search and rescue of persons-in-water based on deep reinforcement learning. Ocean Eng 291:116403. https:\/\/doi.org\/10.1016\/j.oceaneng.2023.116403","journal-title":"Ocean Eng"},{"key":"420_CR27","doi-asserted-by":"publisher","first-page":"101517","DOI":"10.1016\/j.swevo.2024.101517","volume":"86","author":"Y Song","year":"2024","unstructured":"Song Y, Wu Y, Guo Y, Yan R, Suganthan PN, Zhang Y, Pedrycz W, Das S, Mallipeddi R, Ajani OS et al (2024) Reinforcement learning-assisted evolutionary algorithm: a survey and research opportunities. Swarm Evol Comput 86:101517","journal-title":"Swarm Evol Comput"},{"key":"420_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2024.3371964","author":"Y Song","year":"2024","unstructured":"Song Y, Suganthan PN, Pedrycz W, Yan R, Fan D, Zhang Y (2024) Energy-efficient satellite range scheduling using a reinforcement learning-based memetic algorithm. IEEE Trans Aerosp Electr Syst. https:\/\/doi.org\/10.1109\/TAES.2024.3371964","journal-title":"IEEE Trans Aerosp Electr Syst"},{"issue":"4","key":"420_CR29","doi-asserted-by":"crossref","first-page":"172988141986812","DOI":"10.1177\/1729881419868126","volume":"16","author":"F Yao","year":"2019","unstructured":"Yao F, Song Y-J, Zhang Z-S, Xing L-N, Ma X, Li X-J (2019) Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: an improved hybrid genetic algorithm. Int J Adv Rob Syst 16(4):1729881419868126","journal-title":"Int J Adv Rob Syst"},{"key":"420_CR30","volume-title":"Nonparametric statistical methods","author":"M Hollander","year":"2013","unstructured":"Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods. Wiley, Hoboken"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-024-00420-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12293-024-00420-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-024-00420-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T07:21:55Z","timestamp":1726212115000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12293-024-00420-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["420"],"URL":"https:\/\/doi.org\/10.1007\/s12293-024-00420-8","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"28 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}