{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T04:21:55Z","timestamp":1787718115733,"version":"build-2784847793"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University Innovation Team Project of the Department of Education, Anhui Province","award":["2023AH010078"],"award-info":[{"award-number":["2023AH010078"]}]},{"name":"University Key Research Project of the Department of Education, Anhui Province","award":["2024AH051994"],"award-info":[{"award-number":["2024AH051994"]}]},{"name":"High-Level Talent Start-Up Project of West Anhui University","award":["WGKQ2023003"],"award-info":[{"award-number":["WGKQ2023003"]}]},{"name":"Open Project of Anhui Dabieshan Academy of Traditional Chinese Medicine","award":["TCMADM-2024-07"],"award-info":[{"award-number":["TCMADM-2024-07"]}]},{"name":"Natural Science Key Project of West Anhui University","award":["WXZR202307"],"award-info":[{"award-number":["WXZR202307"]}]},{"name":"Natural Science Key Project of West Anhui University","award":["WXZR202312"],"award-info":[{"award-number":["WXZR202312"]}]},{"DOI":"10.13039\/501100020453","name":"West Anhui University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100020453","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/046ft6c74","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Adaptive Reinforced Gray Langur Optimization (ARGLO), an enhanced variant of the Gray Langurs Optimizer, is developed for high-dimensional, multimodal, and nonlinear search landscapes susceptible to local trapping. Although the original GLO performs multi-population cooperative search by simulating the social structures of gray langurs, it still suffers from uneven random initialization, insufficient adaptive population partitioning, weak local perturbation, and premature convergence. ARGLO incorporates three strategies: good point set-based oppositional and quasi-oppositional learning initialization, hierarchical equilibrium adaptive population partitioning, and elite-guided hybrid mutation. Collectively, these mechanisms generate a higher-quality starting population, coordinate global search with local refinement, and reduce the risk of entrapment in suboptimal regions. Evidence from component-wise experiments together with the CEC test suite indicates that ARGLO delivers higher solution precision, steadier convergence, as well as more consistent performance, especially as dimensionality increases. Moreover, ARGLO is applied to near-infrared spectral feature selection and SVR parameter optimization for polysaccharide content prediction in Dendrobium huoshanense. Compared with unoptimized SVR, ARGLO-SVR reduces RMSE by 35.35% and improves R2 by 21.92%; compared with GLO-SVR, it reduces RMSE by 6.05% and improves R2 by 2.30%. These results demonstrate the effectiveness and application potential of ARGLO in complex optimization and rapid nondestructive quality detection of traditional Chinese medicinal materials.<\/jats:p>","DOI":"10.3390\/biomimetics11090604","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T13:20:43Z","timestamp":1787577643000},"page":"604","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Reinforced Gray Langur Optimization for Feature Selection and SVR Modeling of Polysaccharides in Dendrobium huoshanense via NIR Spectroscopy"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0730-3448","authenticated-orcid":false,"given":"Chaochuan","family":"Jia","sequence":"first","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, West Anhui University, Lu\u2019an 237012, China"},{"name":"Anhui Dabie Mountain Institute of Traditional Chinese Medicine, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3142-2517","authenticated-orcid":false,"given":"Feilong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5632-187X","authenticated-orcid":false,"given":"Ting","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical and Optoelectronic Engineering, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maosheng","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui Dabie Mountain Institute of Traditional Chinese Medicine, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, West Anhui University, Lu\u2019an 237012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jia, C.C., Yang, T., Fu, M.S., Liu, Y., Zhou, X., Huang, Z., Wang, F., and Li, W. 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