Szczegóły publikacji
Opis bibliograficzny
A novel balanced binary whale optimization algorithm for dynamic feature selection in green cloud computing / Mateusz SMENDOWSKI, Mateusz WOJTULEWICZ, Piotr NAWROCKI, Leszek RUTKOWSKI // IEEE Transactions on Cloud Computing [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2168-7161 . — 2026 — vol. 14 iss. 2, s. 731–743. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 742–743, Abstr. — Publikacja dostępna online od: 2026-03-09. — L. Rutkowski - dod. afiliacja: Center of Excellence in Artificial Intelligence, AGH University of Krakow, Krakow, Poland; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland. — M. Wojtulewicz - dod. afiliacja: Center of Excellence in Artificial Intelligence, AGH University of Krakow, Krakow, Poland
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168439 |
|---|---|
| Data dodania do BaDAP | 2026-06-19 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TCC.2026.3671450 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Transactions on Cloud Computing |
Abstract
This paper introduces a novel Balanced Binary Whale Optimization Algorithm (BB-WOA) designed specifically for dynamic feature selection in Green Cloud Computing (GCC). Traditional feature selection methods used in cloud resource forecasting often suffer from either suboptimal predictive performance or excessive computational complexity. To address this, we propose significant algorithmic enhancements over the standard Binary Whale Optimization Algorithm (B-WOA), including dynamic binary transition functions, progressive scaling, diversified population initialization via Sobol sequences, balanced exploration-exploitation strategies, and an activation-based recovery mechanism. Our comprehensive experimental evaluation using real-world cloud resource data demonstrates that BB-WOA outperforms existing methods. Specifically, BB-WOA reduces predictive error (RMSE) by up to 7.45% compared to B-WOA and 1.55% compared to Genetic Algorithm (GA). Moreover, BB-WOA achieves computational improvements, reducing execution time by approximately 38.30%, 64.13%, and 78.53% over B-WOA, Random Search (RS), and GA, respectively. Environmentally, the proposed method reduces energy consumption by 38.48% relative to B-WOA, 64.18% compared to RS, and 52.55% relative to GA, while simultaneously selecting significantly fewer features (a reduction of up to 70.77%). These results underscore the effectiveness and sustainability of BB-WOA, positioning it as a highly competitive and environmentally friendly solution.
