{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T05:19:12Z","timestamp":1773465552779,"version":"3.50.1"},"reference-count":137,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-2209806"],"award-info":[{"award-number":["OAC-2209806"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OIA-2148788"],"award-info":[{"award-number":["OIA-2148788"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000\u20132025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. Despite constituting only 9.7% of bio-inspired optimization literature, plant-inspired algorithms demonstrate competitive and often superior performance compared to animal-inspired approaches. Through a meta-analysis of empirical studies, we document that algorithms like Phototropic Growth and Binary Plant Rhizome Growth achieve 97% superiority on CEC2017 benchmarks and 81% accuracy on high-dimensional feature-selection tasks\u2014significantly exceeding established animal-inspired methods like Particle Swarm Optimization and Genetic Algorithms (p &lt; 0.05). However, our review reveals a critical gap: the majority of these algorithms lack the formal theoretical foundations of their counterparts. This paper systematically documents these theoretical deficiencies and positions them as a key area for future research. Our framework maps botanical processes to computational operators, providing structured guidance for future algorithm development. Plant-inspired approaches excel particularly in distributed optimization, resource allocation, and multi-objective problems by leveraging unique mechanisms evolved for survival in sessile, resource-limited environments. These findings establish plant-inspired approaches as a promising yet severely underexplored frontier in optimization theory, with immediate applications in sustainable computing, resilient network design, and resource-constrained artificial intelligence.<\/jats:p>","DOI":"10.3390\/a18110686","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T05:48:46Z","timestamp":1761716926000},"page":"686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Systematic Review of Bio-Inspired Metaheuristic Optimization Algorithms: The Untapped Potential of Plant-Based Approaches"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5210-7278","authenticated-orcid":false,"given":"Hossein","family":"Jamali","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5485-1973","authenticated-orcid":false,"given":"Sergiu M.","family":"Dascalu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0857-6931","authenticated-orcid":false,"suffix":"Jr.","given":"Frederick C.","family":"Harris","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"ref_1","first-page":"137","article-title":"A survey of bio-inspired optimization algorithms","volume":"2","author":"Binitha","year":"2012","journal-title":"Int. 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