Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO)...

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Vydáno v:	Cluster computing Ročník 27; číslo 4; s. 5235 - 5283
Hlavní autoři:	Abdollahzadeh, Benyamin, Khodadadi, Nima, Barshandeh, Saeid, Trojovský, Pavel, Gharehchopogh, Farhad Soleimanian, El-kenawy, El-Sayed M., Abualigah, Laith, Mirjalili, Seyedali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2024 Springer Nature B.V
Témata:	Algorithms Animals Benchmarks Clustering Computer Communication Networks Computer Science Data mining Datasets Heuristic Heuristic methods Linear programming Machine learning Mathematical models Methods Operating Systems Optimization Optimization algorithms Optimization techniques Phase change Popularity Processor Architectures Statistical tests Teaching Global optimization Puma optimization algorithm Automatic phase change Metaheuristic algorithm Optimization Machine learning
ISSN:	1386-7857, 1573-7543
On-line přístup:	Získat plný text
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