{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:55:12Z","timestamp":1774500912379,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62003206"],"award-info":[{"award-number":["62003206"]}]},{"name":"National Natural Science Foundation of China","award":["61973209"],"award-info":[{"award-number":["61973209"]}]},{"name":"National Natural Science Foundation of China","award":["52077213"],"award-info":[{"award-number":["52077213"]}]},{"name":"National Natural Science Foundation of China","award":["62003332"],"award-info":[{"award-number":["62003332"]}]},{"name":"National Natural Science Foundation of China","award":["61902355"],"award-info":[{"award-number":["61902355"]}]},{"name":"National Natural Science Foundation of China","award":["2021358"],"award-info":[{"award-number":["2021358"]}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["62003206"],"award-info":[{"award-number":["62003206"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["61973209"],"award-info":[{"award-number":["61973209"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["52077213"],"award-info":[{"award-number":["52077213"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["62003332"],"award-info":[{"award-number":["62003332"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["61902355"],"award-info":[{"award-number":["61902355"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["2021358"],"award-info":[{"award-number":["2021358"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, including GWO, TLBO, PIO, Jaya, CFPSO, CFWPSO, ETLBO, CTLBO, NTLBO and DOLJaya were used to make comparisons with DOLGWO algorithm. Results indicate that the new model has strong robustness and adaptability, and has the significant advantage of converging to the global optimum, which demonstrates that the DOL strategy greatly improves the performance of original GWO algorithm.<\/jats:p>","DOI":"10.3390\/sym14091871","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T09:51:09Z","timestamp":1662630669000},"page":"1871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer"],"prefix":"10.3390","volume":"14","author":[{"given":"Yang","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"School of Electric Engineering, Shanghai Dianji University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyu","family":"Jin","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6333-8477","authenticated-orcid":false,"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1717-1810","authenticated-orcid":false,"given":"Tianyu","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunlang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6488-224X","authenticated-orcid":false,"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhile","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1137\/0202009","article-title":"Genetic algorithms and the optimal allocation of trials","volume":"2","author":"Holland","year":"1973","journal-title":"SIAM J. 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