A Variable-Fidelity Multi-Objective Evolutionary Method for Polygonal Pin Fin Heat Sink Design

For the multi-objective design of heat sinks, several evolutionary algorithms usually require many iterations to converge, which is computationally expensive. Variable-fidelity multi-objective (VFO) methods were suggested to improve the efficiency of evolutionary algorithms. However, multi-objective problems are seldom optimized using VFO. Therefore, a variable-fidelity evolutionary method (VFMEM) was suggested. Similar to other variable-fidelity algorithms, VFMEM solves a high-fidelity model using a low-fidelity model. Compared with other algorithms, the distinctive characteristic of VFMEM is its application in multi-objective optimization. First, the suggested method uses a low-fidelity model to locate the region where the global optimal solution might be found. Sequentially, both high- and low-fidelity models can be integrated to find the real global optimal solution. Circulation distance elimination (CDE) was suggested to uniformly obtain the PF. To evaluate the feasibility of VFMEM, two classical benchmark functions were tested. Compared with the widely used multi-objective particle swarm optimization (MOPSO), the efficiency of VFMEM was significantly improved and the Pareto frontier (PFs) could also be obtained. To evaluate the algorithm’s feasibility, a polygonal pin fin heat sink (PFHS) design was carried out using VFMEM. Compared with the initial design, the results showed that the mass, base temperature, and temperature difference of the designed optimum heat sink were decreased 5.5%, 18.5%, and 62.0%, respectively. More importantly, if the design was completed directly by MOPSO, the computational cost of the entire optimization procedure would be significantly increased.