Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework

Abstract: The electromagnetic-gravity optimization (EMGO) framework is a novel optimization technique that integrates the fine-structure constant and leverages electromagnetism and gravity principles to achieve efficient and robust optimization solutions. Through comprehensive performance evaluation and comparative analyses against state-of-the-art optimization techniques, EMGO demonstrates superior convergence speed and solution quality. Its unique balance between exploration and exploitation, enabled by the interplay of electromagnetic and gravity forces, makes it a powerful tool for finding optimal or near-optimal solutions in complex problem landscapes. The research contributes by introducing EMGO as a promising optimization approach with diverse applications in engineering, decision support systems, machine learning, data mining, and financial optimization. EMGO’s potential to revolutionize optimization methodologies, handle real-world problems effectively, and balance global exploration and local exploitation establishes its significance. Future research opportunities include exploring adaptive mechanisms, hybrid approaches, handling high-dimensional problems, and integrating machine learning techniques to enhance its capabilities further. EMGO gives a novel approach to optimization, and its efficacy, advantages, and potential for extensive adoption open new paths for advancing optimization in many scientific, engineering, and real-world domains.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework ; volume:33 ; number:1 ; year:2024 ; extent:19 ; Journal of intelligent systems ; 33, Heft 1 (2024) (gesamt 19)

Beteiligte, Orts- und Zeitangaben

• Urheber: Akhtar, Md. Amir Khusru ; Kumar, Mohit ; Verma, Sahil ; Cengiz, Korhan ; Verma, Pawan Kumar ; Khurma, Ruba Abu ; Alazab, Moutaz

Weitere Informationen

• DOI: 10.1515/jisys-2023-0306
• URN: urn:nbn:de:101:1-2412181435208.687630103909
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:24 MESZ