{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T08:14:48Z","timestamp":1781252088923,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.<\/jats:p>","DOI":"10.3390\/e23091189","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T12:20:37Z","timestamp":1631190037000},"page":"1189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection"],"prefix":"10.3390","volume":"23","author":[{"given":"Rehab Ali","family":"Ibrahim","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2203-4549","authenticated-orcid":false,"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[{"name":"Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed A.","family":"Ewees","sequence":"additional","affiliation":[{"name":"Department of Computer, Damietta University, Damietta 34517, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-7641","authenticated-orcid":false,"given":"Mohammed A. A.","family":"Al-qaness","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6551-2371","authenticated-orcid":false,"given":"Dalia","family":"Yousri","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-7890","authenticated-orcid":false,"given":"Samah","family":"Alshathri","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7682-6269","authenticated-orcid":false,"given":"Mohamed","family":"Abd Elaziz","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"},{"name":"Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113122","DOI":"10.1016\/j.eswa.2019.113122","article-title":"Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection","volume":"145","author":"Tubishat","year":"2020","journal-title":"Expert Syst. 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