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The use of nanofluids as coolant fluid in the microchannel heat sink (MCHS) is an effective technique for improving the thermal performance of electronic devices. A comparative study is performed with the help of a multi-objective genetic algorithm (MOGA) to find the optimal geometric variables of the MCHS and choose the appropriate nanofluid. For that purpose, four practical nanofluids, including Al 2 O 3 -water, Cu-water, SiO 2 -water, and carbon nanotube (CNT)-water, are thoroughly investigated. Simultaneously minimization of the total thermal resistance and pumping power consumption (POW) are considered as the optimization goal, and the MOGA is employed to achieve the optimal solution. To check the accuracy of the thermal resistance modeling and assessing the optimization algorithm, several case studies with a different number of optimization variables are defined to investigate the capability of the algorithm in finding the soptimal microchannel design variables and choosing the suitable nanofluid. The optimization variables consist of the channel aspect and wall ratios, base thickness, nanoparticles volume concentration, nanoparticles diameter, and the volume flow rate. Compared to other nanofluids, CNT provides better thermal performance. Furthermore, increasing the volume concentration of nanoparticles enhances thermal performance, which can also be achieved through the reduction of nanoparticles diameter.

Simultaneous Optimization of Geometric and Nanofluids Parameters in a Rectangular Microchannel Heat Sink