{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:03:57Z","timestamp":1791392637419,"version":"4.3.3"},"reference-count":57,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82272075"],"award-info":[{"award-number":["82272075"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm\u2019s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems.<\/jats:p>","DOI":"10.3390\/biomimetics9120727","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T08:15:54Z","timestamp":1732781754000},"page":"727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6494-3672","authenticated-orcid":false,"given":"Guoping","family":"You","sequence":"first","affiliation":[{"name":"School of Information Engineering, Jiangxi Science and Technology Normal University, Nanchang 330000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengtong","family":"Lu","sequence":"additional","affiliation":[{"name":"Ruijie Networks Co., Ltd., Fuzhou 350000, China"},{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1007\/s00521-019-04132-w","article-title":"A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems","volume":"32","author":"Ezugwu","year":"2020","journal-title":"Neural Comput. 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