Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

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dc.authoridSeyyedabbasi, Amir/0000-0001-5186-4499
dc.authoridWei, Guo/0000-0001-9988-0498
dc.contributor.authorHu, Gang
dc.contributor.authorHuang, Feiyang
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorWei, Guo
dc.date.accessioned2024-05-19T14:46:10Z
dc.date.available2024-05-19T14:46:10Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [51875454, 62376212]en_US
dc.description.sponsorshipAcknowledgments This work is supported by the National Natural Science Foundation of China (grant nos. 51875454 and 62376212) .en_US
dc.identifier.doi10.1016/j.apm.2024.03.001
dc.identifier.endpage271en_US
dc.identifier.issn0307-904X
dc.identifier.issn1872-8480
dc.identifier.scopus2-s2.0-85187545427en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage243en_US
dc.identifier.urihttps://doi.org10.1016/j.apm.2024.03.001
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5460
dc.identifier.volume130en_US
dc.identifier.wosWOS:001207093100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofApplied Mathematical Modellingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectPath Planningen_US
dc.subjectUnmanned Aerial Vehicleen_US
dc.subjectConstraint Conditionsen_US
dc.subjectBottlenose Dolphin Optimizeren_US
dc.subjectRandom Mixed Mutation Strategyen_US
dc.subjectChaotic Opposition -Based Learning Strategyen_US
dc.titleEnhanced multi-strategy bottlenose dolphin optimizer for UAVs path planningen_US
dc.typeArticleen_US

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