Tailoring Metaheuristics for Designing Thermodynamic-Optimal Cooling Devices for Microelectronic Thermal Management Applications

Heat sinks are a prevalent and direct solution for addressing the Microelectronic Thermal Management Problem (MTMP), which is critical in today's electronic industry. Specifi-cally, an optimally designed thermodynamic heat sink ensures that microelectronics operate reliably without compromising their lifespan and performance, thereby indirectly safeguarding user safety. Although Metaheuristics (MHs) have proven effective in tackling this complex design challenge due to their robust characteristics, no single MH consistently delivers superior per-formance across all scenarios. The study explores the feasibility of an Automated Metaheuristic Design strategy, employing a hyper-heuristic search to develop a population-based, metaphor-free MH specifically for the MTMP. Various scenarios are assessed by varying the heat sink design specifications and benchmarking the custom MH designs against several state-of-the-art MHs. The findings of this preliminary work provide statistical evidence that the tailored MHs surpass the performance of established MHs in these scenarios. A toolkit of MH components is assembled, which can be customized to construct MHs specifically for MTMPs. This approach enables practitioners to select the most suitable solver for a particular problem without needing extensive expertise in heuristic-based optimization.