Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Olatunji Akinola"'
Publikováno v:
Results in Control and Optimization, Vol 11, Iss , Pp 100225- (2023)
There is a surge in the application of population-based metaheuristic algorithms to find the optimal feature subset from high dimensional datasets. Many of these approaches cannot properly scale especially as they are expected to maintain two opposin
Externí odkaz:
https://doaj.org/article/ed759c792a274f418f7a6fedaed26f48
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0275346 (2022)
This paper proposes an improvement to the dwarf mongoose optimization (DMO) algorithm called the advanced dwarf mongoose optimization (ADMO) algorithm. The improvement goal is to solve the low convergence rate limitation of the DMO. This situation ar
Externí odkaz:
https://doaj.org/article/3e2666ca426b4bf481707bd80dd277cd
Publikováno v:
Applied Sciences, Vol 12, Iss 22, p 11787 (2022)
In the past decade, the extraction of valuable information from online biomedical datasets has exponentially increased due to the evolution of data processing devices and the utilization of machine learning capabilities to find useful information in
Externí odkaz:
https://doaj.org/article/7c6e685968df4c11905e85d3bc8b55b0
Autor:
Jeffrey O. Agushaka, Absalom E. Ezugwu, Oyelade N. Olaide, Olatunji Akinola, Raed Abu Zitar, Laith Abualigah
Publikováno v:
Journal of bionic engineering.
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::220511f43a08782658cc3a0aa1b6ce98
https://pubmed.ncbi.nlm.nih.gov/36530517
https://pubmed.ncbi.nlm.nih.gov/36530517