Accurate prediction of thermal conductivity in (nano-PEG) composites is essential for accelerating thermal management material design. This study develops a hybrid Random Forest (RF) framework optimized using eight evolutionary algorithms, including (PSO), (GA), (WOA), (GWO), (CSA), (FPA), (FA), and (BA). A dataset of 229 experimental observations was used to model thermal conductivity as a function of temperature, PEG molecular weight, nanoparticle concentration, and nanoparticle form. Among evaluated models, the Bat Algorithm-optimized RF (RF-BA) achieved highest predictive efficiency with R 2 = 0.995406, MSE = 0.000196, and AARE = 1.291%, while the PSO-optimized model (RF-PSO) demonstrated the fastest optimization runtime (96.9 s) with competitive accuracy. Correlation and SHAP analyses revealed nanoparticle concentration as the dominant factor governing thermal conductivity (correlation coefficient = 0.75), followed by PEG molecular weight (0.56), temperature (0.33), and nanoparticle form (0.24). The results demonstrate that evolutionarily optimized ensemble learning provides a reliable and computationally efficient strategy for thermophysical property prediction in nano-PEG composites, offering a practical alternative to extensive experimental characterization.
Tao et al. (Tue,) studied this question.