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Consequently, researchers have proposed numerous large-scale multi-objective evolutionary algorithms (LMOEAs) to address these problems. Among them, neural network (NN)-based LMOEAs have garnered significant attention owing to their superior search efficiency. Regrettably, existing NN-based LMOEAs are limited by a single source of training data and pre-fixed network topology, resulting in insufficient generalization and weak adaptability. We propose a heterogeneous operator mechanism and stochastic configuration network-enhanced LMOEA (HMSCN-LMOEA) to overcome these issues. Specifically, a heterogeneous operator mechanism is introduced to address the limited data sources in NN model training, thereby effectively enhancing the quality of the training set. A stochastic configuration network (SCN) model with dynamic structural adaptability is designed to replace the NN model with a pre-fixed network topology, thereby enhancing the algorithm\u2019s adaptability. Finally, to evaluate the algorithm\u2019s performance, four large-scale multi-objective optimization problems\u2014UF (Unconstrained Problem), WFG (Walking Fish Group), LSMOP (Large-Scale Multi-Objective Test Problem), and ZCAT (Zapotecas, Coello, Aguirre, and Tanaka)\u2014are adopted. Experimental results based on Inverted generational distance (IGD), Inverted generational distance plus (IGD+), Spacing, and hypervolume (HV) metrics demonstrate the significant advantages and competitiveness of HMSCN-LMOEA over state-of-the-art LMOEAs. Furthermore, HMSCN-LMOEA is employed to solve the cloud task scheduling problem across different task scales, and the experimental results show that it achieves the top rank in 75% of the evaluated scenarios, further demonstrating its promising potential.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf144","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:59:08Z","timestamp":1767178748000},"page":"1-44","source":"Crossref","is-referenced-by-count":1,"title":["Heterogeneous operator mechanism and stochastic configuration network-enhanced large-scale multi-objective evolutionary algorithm"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6094-8221","authenticated-orcid":false,"given":"Fengbin","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University , Huaxi, Guiyang, Guizhou 550025 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,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0227-020X","authenticated-orcid":false,"given":"Panliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University , Huaxi, Guiyang, Guizhou 550025 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8061-9195","authenticated-orcid":false,"given":"Yang","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University , Huaxi, Guiyang, Guizhou 550025 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7427-8749","authenticated-orcid":false,"given":"Libang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guizhou University , Huaxi, Guiyang, Guizhou 550025 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