{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:34:13Z","timestamp":1787027653547,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Institute of Marine Science &amp; Technology Promotion (KIMST)","award":["RS-2022-KS221629"],"award-info":[{"award-number":["RS-2022-KS221629"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>This study proposes a biomimetic optimization approach for maritime Search and Rescue (SAR) planning using a Genetic Algorithm (GA). The goal is to maximize the number of detected drifting targets by optimally deploying both official and civilian Search and Rescue Units (SRUs). The proposed method incorporates a POD-adjusted fitness function with collision-avoidance constraints and is enhanced by a greedy initialization strategy. To validate its effectiveness, we compare the GA against a baseline method (EAGD) that combines a (1 + 1)-Evolutionary Algorithm with greedy deployment, across 24 experiments involving 2 realistic maritime scenarios and 12 coverage conditions. Results show that GA consistently achieves higher average fitness and stability, particularly under stress-test settings involving only civilian vessels. The findings underscore the potential of biomimetic algorithms for real-time, flexible, and scalable SAR planning, while highlighting the value of civilian participation in emergency maritime operations.<\/jats:p>","DOI":"10.3390\/biomimetics10090588","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T15:15:57Z","timestamp":1756912557000},"page":"588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimizing Maritime Search and Rescue Planning via Genetic Algorithms: Incorporating Civilian Vessel Collaboration"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5806-3401","authenticated-orcid":false,"given":"Seung-Yeol","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0492-0889","authenticated-orcid":false,"given":"Yong-Hyuk","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","unstructured":"International Maritime Organization (2019). 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Ecol."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/9\/588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:38:54Z","timestamp":1760035134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/9\/588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["biomimetics10090588"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics10090588","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,3]]}}}