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The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a classic algorithm in the field of multi-objective optimization. However, this algorithm frequently lacks the adaptive capability required to navigate high-dimensional solution spaces, often trapping the search in local optima, particularly when constrained by practical energy states of heterogeneous machines. To address these complexities, this study proposes a hybrid algorithm, named QGN, integrating Q-learning, the Grey Wolf Optimizer (GWO), and the NSGA-II. Specifically, QGN algorithm integrates NSGA-II for robust diversity maintenance with GWO for high-precision intensification. Unlike static hybrid methods, QGN employs a Q-learning agent as an adaptive controller to dynamically balance global exploration and local refinement, providing a theoretically grounded response to the rugged search landscape created by machine heterogeneity. Comprehensive experimental validation across diverse production scenarios confirms that QGN significantly outperforms baselines, including NSGA-II, Jaya, and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA\/D), as well as the state-of-the-art Q-learning- and GVNS-driven NSGA-II (QVNS) algorithm, in terms of both convergence and diversity. The results indicate that the proposed algorithm yields superior solution dominance, generates a substantially larger set of non-dominated solutions, and maintains a more uniform distribution along the Pareto front.<\/jats:p>","DOI":"10.3390\/axioms15030193","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T13:00:02Z","timestamp":1772802002000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Synergistic Evolutionary Optimization with Reinforcement Learning for Multi-Objective Energy-Efficient Hybrid Flow Shop Scheduling"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuchen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5222-7608","authenticated-orcid":false,"given":"Ting","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuesong","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinsong","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1080\/00207543.2018.1444806","article-title":"Industry 4.0: State of the art and future trends","volume":"56","author":"Xu","year":"2018","journal-title":"Int. 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