Akhtar, Md Amir KhusruKumar, MohitVerma, SahilCengiz, KorhanVerma, Pawan KumarKhurma, Ruba AbuAlazab, Moutaz2025-04-162025-04-162024Akhtar, M. A. K., Kumar, M., Verma, S., Cengiz, K., Verma, P. K., Khurma, R. A., & Alazab, M. (2024). Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework. Journal of Intelligent Systems, 33(1), 20230306.0334-18602191-026Xhttp://dx.doi.org/10.1515/jisys-2023-0306https://hdl.handle.net/20.500.12713/6042The 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.eninfo:eu-repo/semantics/openAccessConvergence SpeedElectromagnetismFine-Structure ConstantGravityOptimization TechniqueArticle331119WOS:0013794545000012-s2.0-85213011149N/A10.1515/jisys-2023-0306Q2