{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xie C"],"funding":["Guiyang Science and Technology Platform Construction Project","National important project under grant No.2020YFB1713300","Guizhou Provincial Department of Science and Technology Project under grant No.QKHZYD[2023]002","National Natural Science Foundation of China's top- level program under grant"],"pagination":["21734"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11408698"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_abstract":["With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. The"],"journal":["Scientific reports"],"pubmed_title":["Multiple elite strategy enhanced RIME algorithm for 3D UAV path planning."],"pmcid":["PMC11408698"],"funding_grant_id":["No.ZKHT[2023]7-2","No.QKHZYD[2023]002","No.2020YFB1713300","No.52275480"],"pubmed_authors":["Fu S","Li S","Zhang X","Qin X","Xie C"],"additional_accession":[]},"is_claimable":false,"name":"Multiple elite strategy enhanced RIME algorithm for 3D UAV path planning.","description":"With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. The","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-04T01:03:16.429Z","creation":"2025-04-04T01:03:16.429Z"},"accession":"S-EPMC11408698","cross_references":{"pubmed":["39289426"],"doi":["10.1038/s41598-024-72279-1"]}}