{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:29:20Z","timestamp":1780496960422,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2024JJ7584"],"award-info":[{"award-number":["2024JJ7584"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Project of the Education Department of Hunan Province","award":["25A0511"],"award-info":[{"award-number":["25A0511"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Precision irrigation is crucial for sustainable agriculture, yet conventional single-objective optimization methods struggle to balance conflicting demands such as crop yield, operational cost, and environmental sustainability. This study introduces an Adaptive Multi-Strategy Grey Wolf Optimizer with Reinforcement Learning for Multi-Objective Optimization (AMSGWO-RL-MO) to enhance precision irrigation decision-making. AMSGWO-RL-MO integrates four strategies: standard GWO exploitation, L\u00e9vy flight exploration, differential evolution-based diversity enhancement, and Stochastic Elite Opposition-Based Learning. A Q-learning mechanism dynamically adjusts these strategies, adapting to real-time search conditions to select the optimal approach. We constructed a comprehensive three-objective framework incorporating soil moisture dynamics, crop growth models, and environmental impact assessments. Experimental simulations over a 40-day growth cycle demonstrate AMSGWO-RL-MO\u2019s rapid convergence by the sixth generation, consistently achieving a high-quality Pareto front across 30 independent runs. The knee-point solution yielded a mean crop yield of 96.96%, outperforming standard GWO and multi-strategy variants by approximately 3.8%. Statistical analysis confirms its superior robustness and well-distributed solutions along the Pareto front. These results indicate that the RL-driven adaptive mechanism effectively balances exploration and exploitation. The proposed method offers a more diverse array of Pareto-optimal solutions, presenting a broader trade-off space for balancing crop yield and environmental sustainability compared to traditional weighted-sum approaches. This enhancement facilitates scientific agricultural decision-making under various operational constraints.<\/jats:p>","DOI":"10.3390\/a19030168","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:58:18Z","timestamp":1771952298000},"page":"168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Multi-Strategy Grey Wolf Optimizer with Reinforcement Learning for Multi-Objective Precision Irrigation Optimization"],"prefix":"10.3390","volume":"19","author":[{"given":"Guangluan","family":"Yin","sequence":"first","affiliation":[{"name":"School of Information, Yongzhou Vocational Technical College, Yongzhou 425000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8035-5626","authenticated-orcid":false,"given":"Wuke","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0396-4836","authenticated-orcid":false,"given":"Qi","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"ref_1","unstructured":"Faur\u00e8s, J.M., Hoogeveen, J., and Bruinsma, J. 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