{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T15:10:09Z","timestamp":1762528209263,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement\u2014a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best\/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare L\u00e9vy-flight kicks enable long escapes. A reflection\/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems.<\/jats:p>","DOI":"10.3390\/computers14110486","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T14:55:15Z","timestamp":1762527315000},"page":"486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ripple Evolution Optimizer: A Novel Nature-Inspired Metaheuristic"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-3291","authenticated-orcid":false,"given":"Hussam N.","family":"Fakhouri","sequence":"first","affiliation":[{"name":"Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-8395","authenticated-orcid":false,"given":"Hasan","family":"Rashaideh","sequence":"additional","affiliation":[{"name":"Department of Computer Science Al-Balqa Applied University, Al-Salt 19117, Jordan"},{"name":"Faculty of Information Technology, Aqaba University of Technology, Aqaba 77110, Jordan"}]},{"given":"Riyad","family":"Alrousan","sequence":"additional","affiliation":[{"name":"Design and Visual Communication Department, School of Architecture and Built Environment (SABE), German Jordanian University (GJU), Amman 11180, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7548-0529","authenticated-orcid":false,"given":"Faten","family":"Hamad","sequence":"additional","affiliation":[{"name":"Information Studies Department, Sultan Qaboos University, Muscat 123, Oman"},{"name":"Information Sciences and Educational Technology Department, School of Educational Sciences, The University of Jordan, Amman 11196, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2013-6923","authenticated-orcid":false,"given":"Zaid","family":"Khrisat","sequence":"additional","affiliation":[{"name":"Faculty of Arts and Educational Sciences; Middle East University, Amman 11831, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.eswa.2016.06.044","article-title":"A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms","volume":"63","author":"Bhandari","year":"2016","journal-title":"Expert Syst. 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