Baghoolizadeh, MohammadrezaSalahshour, SoheılPirmoradian, MostafaSajadi, S. MohammadSalahshour, SoheilBaghaei, Sh.2024-05-252024-05-25202400301-679X1879-246410.1016/j.triboint.2024.1095822-s2.0-85189760812https://doi.org/10.1016/j.triboint.2024.109582https://hdl.handle.net/20.500.14517/1174Baghoolizadeh, Mohammadreza/0000-0002-3703-0866Genetic algorithms and machine learning methods can accurately anticipate hybrid nanofluids' complicated rheology. Scientists and engineers can understand hybrid materials by using genetic algorithms to optimize and machine learning to discover complicated relationships between input variables and rheological responses. As a continuation of the author's previous research on the rheological properties of a nano-lubricant based on engine oil and hybrid nanoparticles, this study uses machine learning and genetic algorithms to theoretically assess the dynamic viscosity of the MWCNT-MgO/oil SAE 50 hybrid nanofluid and identify optimal properties. MLR, DTree, Ridge, PLR, SVM, Lasso, ECR, GPR, and MPR are used for regression analysis. Best multi-objective issue solutions are represented by the Pareto front. The NSGA-II algorithm determines the Pareto front. The MPR and NSGA-II algorithms provide a Pareto front with the most precise optimal spot boundaries. The Weighted Sum Method (WSM) simplifies multi-objective problems into single-objective problems, making optimal solutions easier to find. The results show that the maximum margin of deviation for mu nf and tau is - 2.5615 and - 5.239, respectively. According to the Taylor chart, the best mu nf mode for R, RMSE and STD is equal to 0.9983, 7.6639, 130.0056. Also, these values for tau are equal to 0.9996, 15.4515, and 516.0219.eninfo:eu-repo/semantics/closedAccessHybrid nanofluidRheologyMachine learningGenetic algorithmsArticleQ1Q1195WOS:001217634500001