{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T08:10:45Z","timestamp":1790928645392,"version":"4.1.0"},"reference-count":21,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Addressing the insufficient optimization performance in drone 3D path planning and the issues of inadequate optimization precision and tendency to fall into local optima in the existing Whale Optimization Algorithm (WOA), this paper proposes a drone 3D path planning method based on an improved Whale Optimization Algorithm (CSRD-WOA). Firstly, to enhance the search efficiency and fitness accuracy of the Whale Algorithm, the Cuckoo Search and Random Differential Strategy were introduced and compared with the traditional Particle Swarm Optimization algorithm, Whale Algorithm, and Cuckoo Search Algorithm. Experimental results demonstrate that the CSRD-WOA algorithm improves global search capabilities and prevents premature convergence, significantly enhancing optimization precision and convergence speed. Secondly, applying the CSRD-WOA algorithm to drone 3D path planning issues, the simulation results show that the CSRD-WOA algorithm can effectively manage path planning in complex terrains, showcasing its application potential in drone path planning.<\/jats:p>","DOI":"10.1371\/journal.pone.0316836","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T13:52:37Z","timestamp":1740405157000},"page":"e0316836","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":20,"title":["Research on three-dimensional path planning of unmanned aerial vehicle based on improved Whale Optimization 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