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Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.<\/jats:p>","DOI":"10.1007\/s40747-023-01335-6","type":"journal-article","created":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T14:02:08Z","timestamp":1707573728000},"page":"3459-3471","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling"],"prefix":"10.1007","volume":"10","author":[{"given":"Fayong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Caixian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-6865","authenticated-orcid":false,"given":"Wenyin","family":"Gong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"issue":"1","key":"1335_CR1","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/TII.2023.3271749","volume":"20","author":"C Lu","year":"2024","unstructured":"Lu C, Gao R, Yin L, Zhang B (2024) Human-robot collaborative scheduling in energy-efficient welding shop. 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