Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor Networks

For the shortcomings of the manta ray foraging optimization (MRFO) algorithm, like slow convergence speed and difficult to escape from the local optimum, an improved manta ray foraging algorithm based on Latin hypercube sampling and group learning is proposed. Firstly, the Latin hypercube sampling (...

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Detalles Bibliográficos
Autores principales: Zhu, Fang, Wang, Wenhao, Li, Shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262499/
https://www.ncbi.nlm.nih.gov/pubmed/35814539
http://dx.doi.org/10.1155/2022/3082933

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262499/
https://www.ncbi.nlm.nih.gov/pubmed/35814539
http://dx.doi.org/10.1155/2022/3082933

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