An improved chaos sparrow search algorithm for UAV path planning.

Journal

Scientific reports

ISSN: 2045-2322

Titre abrégé: Sci Rep

Pays: England

ID NLM: 101563288

Informations de publication

Date de publication:
03 Jan 2024

Historique:

received: 19 09 2023

accepted: 20 12 2023

medline: 4 1 2024

pubmed: 4 1 2024

entrez: 3 1 2024

Statut: epublish

Résumé

This study suggests an improved chaos sparrow search algorithm to overcome the problems of slow convergence speed and trapping in local optima in UAV 3D complex environment path planning. First, the quality of the initial solutions is improved by using a piecewise chaotic mapping during the population initialization phase. Secondly, a nonlinear dynamic weighting factor is introduced to optimize the update equation of producers, reducing the algorithm's reliance on producer positions and balancing its global and local exploration capabilities. In the meantime, an enhanced sine cosine algorithm optimizes the update equation of the scroungers to broaden the search space and prevent blind searches. Lastly, a dynamic boundary lens imaging reverse learning strategy is applied to prevent the algorithm from getting trapped in local optima. Experiments of UAV path planning on simple and complex maps are conducted. The results show that the proposed algorithm outperforms CSSA, SSA, and PSO algorithms with a respective time improvement of 22.4%, 28.8%, and 46.8% in complex environments and exhibits high convergence accuracy, which validates the proposed algorithm's usefulness and superiority.

Identifiants

DOI: 10.1038/s41598-023-50484-8 PMID: 38172279

pubmed: 38172279

doi: 10.1038/s41598-023-50484-8

pii: 10.1038/s41598-023-50484-8

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

366

Subventions

Organisme : Changsha University of Science and Technology major school-enterprise cooperation fund

ID : 30404022264

Organisme : Changsha University of Science and Technology major school-enterprise cooperation fund

ID : 30404022264

Informations de copyright

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