{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T01:58:31Z","timestamp":1775008711905,"version":"3.50.1"},"reference-count":99,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration\/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at https:\/\/github.com\/MohammedQaraad\/GMSMFO-algorithm.<\/jats:p>","DOI":"10.1093\/jcde\/qwad053","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T04:48:26Z","timestamp":1686718106000},"page":"1363-1389","source":"Crossref","is-referenced-by-count":21,"title":["Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection"],"prefix":"10.1093","volume":"10","author":[{"given":"Nazar K","family":"Hussein","sequence":"first","affiliation":[{"name":"Department of Mathematics, College of Computer Sciences and Mathematics, Tikrit University , Tikrit 34001 , Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9513-0355","authenticated-orcid":false,"given":"Mohammed","family":"Qaraad","sequence":"additional","affiliation":[{"name":"TIMS, FS, Abdelmalek Essaadi University , Tetouan 93000 , Morocco"},{"name":"Department of Computer Science, Faculty of Science, Amran University , Amran 891-6162 , Yemen"}]},{"given":"Souad","family":"Amjad","sequence":"additional","affiliation":[{"name":"TIMS, FS, Abdelmalek Essaadi University , Tetouan 93000 , Morocco"}]},{"given":"M A","family":"Farag","sequence":"additional","affiliation":[{"name":"Department of Basic Engineering Science, Faculty of Engineering, Menoufia University , Shebin El-Kom 32951 , Egypt"}]},{"given":"Saima","family":"Hassan","sequence":"additional","affiliation":[{"name":"Institute of Computing, Kohat University of Science and Technology , Kohat 26000 , Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1443-9458","authenticated-orcid":false,"given":"Seyedali","family":"Mirjalili","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia , Brisbane 4001 , Australia"},{"name":"Yonsei Frontier Lab, Yonsei University , Seoul 01008 , South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1259-6193","authenticated-orcid":false,"given":"Mostafa A","family":"Elhosseini","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University , Yanbu 46411 , Saudi Arabia"},{"name":"Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University , Mansoura 35516 , Egypt"}]}],"member":"286","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"2023070709002299700_bib1","doi-asserted-by":"crossref","first-page":"112824","DOI":"10.1016\/j.eswa.2019.112824","article-title":"A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection","volume":"139","author":"Abdel-Basset","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"2023070709002299700_bib2","doi-asserted-by":"crossref","first-page":"7165","DOI":"10.1007\/s00521-020-05483-5","article-title":"An intelligent feature selection 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