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To address this, we propose Multi-Objective Cooperative Multi-Fitness (MOCMF), a novel mechanism that significantly enhances multi-objective evolutionary algorithms through a unique cooperative evaluation and recoding strategy. Diverging from existing multi-decoder approaches, MOCMF\u2019s core innovation lies in its collaborative framework: heuristic decoders work in tandem to support a baseline decoding function, providing expert solutions that guide the Lamarckian recoding of chromosomes. Furthermore, MOCMF extends this cooperative evaluation to a multi-objective setting, where each heuristic decoder focuses on optimising a specific objective, leading to the generation of multiple distinct solutions per chromosome. Experimental results on data-intensive workflow benchmarks show that MOCMF improves the average Hypervolume by 32% and Inverted Generational Distance by 42% compared to a standard NSGA-II implementation, and by 7% and 6% respectively compared to its mono-objective cooperative variants. The proposed mechanism is also generalisable and potentially applicable to other multi-objective problems beyond workflow scheduling.<\/jats:p>","DOI":"10.1177\/10692509251363797","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T07:12:56Z","timestamp":1754896376000},"page":"443-464","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiobjective cooperative multi-fitness in workflow scheduling problem"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4330-2248","authenticated-orcid":false,"given":"Pablo","family":"Barredo","sequence":"first","affiliation":[{"name":"Dep. of Computer Science, University of Oviedo. 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