Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework
<p dir="ltr">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 per...
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2024
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| _version_ | 1864513540580179968 |
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| author | Md. Amir Khusru Akhtar (22155568) |
| author2 | Mohit Kumar (399134) Sahil Verma (10776715) Korhan Cengiz (19450537) Pawan Kumar Verma (22155571) Ruba Abu Khurma (22155574) Moutaz Alazab (17730060) |
| author2_role | author author author author author author |
| author_facet | Md. Amir Khusru Akhtar (22155568) Mohit Kumar (399134) Sahil Verma (10776715) Korhan Cengiz (19450537) Pawan Kumar Verma (22155571) Ruba Abu Khurma (22155574) Moutaz Alazab (17730060) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Amir Khusru Akhtar (22155568) Mohit Kumar (399134) Sahil Verma (10776715) Korhan Cengiz (19450537) Pawan Kumar Verma (22155571) Ruba Abu Khurma (22155574) Moutaz Alazab (17730060) |
| dc.date.none.fl_str_mv | 2024-12-18T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1515/jisys-2023-0306 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Unifying_optimization_forces_Harnessing_the_fine-structure_constant_in_an_electromagnetic-gravity_optimization_framework/30023722 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Mathematical sciences Applied mathematics fine-structure constant electromagnetism gravity convergence speed optimization technique |
| dc.title.none.fl_str_mv | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Intelligent Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1515/jisys-2023-0306" target="_blank">https://dx.doi.org/10.1515/jisys-2023-0306</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8d70f5e4b8a43d46d2f40730e5be17e4 |
| identifier_str_mv | 10.1515/jisys-2023-0306 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30023722 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization frameworkMd. Amir Khusru Akhtar (22155568)Mohit Kumar (399134)Sahil Verma (10776715)Korhan Cengiz (19450537)Pawan Kumar Verma (22155571)Ruba Abu Khurma (22155574)Moutaz Alazab (17730060)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningMathematical sciencesApplied mathematicsfine-structure constantelectromagnetismgravityconvergence speedoptimization technique<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Intelligent Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1515/jisys-2023-0306" target="_blank">https://dx.doi.org/10.1515/jisys-2023-0306</a></p>2024-12-18T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1515/jisys-2023-0306https://figshare.com/articles/journal_contribution/Unifying_optimization_forces_Harnessing_the_fine-structure_constant_in_an_electromagnetic-gravity_optimization_framework/30023722CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300237222024-12-18T09:00:00Z |
| spellingShingle | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework Md. Amir Khusru Akhtar (22155568) Information and computing sciences Artificial intelligence Data management and data science Machine learning Mathematical sciences Applied mathematics fine-structure constant electromagnetism gravity convergence speed optimization technique |
| status_str | publishedVersion |
| title | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| title_full | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| title_fullStr | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| title_full_unstemmed | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| title_short | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| title_sort | Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Mathematical sciences Applied mathematics fine-structure constant electromagnetism gravity convergence speed optimization technique |