Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Jeffrey O Agushaka"'
Publikováno v:
PLoS ONE, Vol 18, Iss 3, p e0282812 (2023)
Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. The evaluation and suitability of these selected features are often analyzed using classifiers. T
Externí odkaz:
https://doaj.org/article/7b1e927314114336909f78c12e5c07b1
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:
PLoS ONE, Vol 17, Iss 10, p e0274850 (2022)
Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model's accuracy and improve classification performance w
Externí odkaz:
https://doaj.org/article/1b11555058eb4b11aba952d2b4fc6587
Autor:
Jeffrey O Agushaka, Absalom E Ezugwu
Publikováno v:
PLoS ONE, Vol 16, Iss 8, p e0255703 (2021)
The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used to test its perfor
Externí odkaz:
https://doaj.org/article/dd71944f3d834263a413dde31dd503e1
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-22 (2022)
Abstract The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Stil
Externí odkaz:
https://doaj.org/article/8a5e5f6b983449cca33682132edf73ae
Autor:
Jeffrey O. Agushaka, Absalom E. Ezugwu, Laith Abualigah, Samaher Khalaf Alharbi, Hamiden Abd El-Wahed Khalifa
Publikováno v:
Archives of Computational Methods in Engineering. 30:1727-1787
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa218a500f80c86d479f85977626122d
https://doi.org/10.1007/s11831-022-09850-4
https://doi.org/10.1007/s11831-022-09850-4
Publikováno v:
Neural Computing and Applications. 35:4099-4131
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9d62376f4fdab513da4a585365dd910
https://doi.org/10.1007/s00521-022-07854-6
https://doi.org/10.1007/s00521-022-07854-6
Publikováno v:
Neural Computing and Applications. 34:20017-20065
This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two commo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f36e62ebac668b7edad5c74ac747e0a
https://doi.org/10.1007/s00521-022-07530-9
https://doi.org/10.1007/s00521-022-07530-9
Autor:
Jeffrey O. Agushaka, Absalom E. Ezugwu
Publikováno v:
Journal of Intelligent Systems, Vol 31, Iss 1, Pp 70-94 (2021)
Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3eafd50909b94b67e58bb1b72eaead8b
https://doaj.org/article/d31c688b4ec0499db42d2469be542655
https://doaj.org/article/d31c688b4ec0499db42d2469be542655
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