# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 2 Literature Review
[14] had introduced Adaptive Particle Swarm Optimization (APSO) as an alternative to traditional methods, the original Particle Swarm Optimization (PSO) [15]. This approach dynamically adjusted parameters to improve search efficiency in nonlinear spaces. However, despite improvements in convergence times, slow convergence in complex thermal models persisted, making APSO less applicable to real-time scenarios. [PERSON] et al. (2013) [16] expanded on evolutionary algorithms for correlating thermal balance test results with mathematical models. These algorithms, like GAs, excelled at finding global optima but required significant computational resources and parameter tuning. Similarly, [PERSON] (2014) [17; 18] explored Quasi-Newton algorithms from the Broyden class [19], which offered faster convergence with fewer iterations. However, these methods still struggled with the non-convex nature of most thermal models, limiting their application to simpler problems. In 2017, [PERSON] et al. [20] conducted a comparative study of gradient-based methods and GAs, concluding that GAs were more suitable for non-convex, nonlinear problems typical of spacecraft thermal models. Gradient-based methods, while faster, were less effective in complex scenarios. [PERSON] and [PERSON] (2016) [21] continued to explore GAs in spacecraft thermal model correlation, particularly in experiments on the International Space Station. Their 2022 study [22] further demonstrated the robustness of GAs, even under measurement uncertainty, though at the cost of increased computational demand. In 2023, [PERSON] et al. [23] introduced a hybrid method combining Multiple Linear Regression (MLR) with optimization algorithms, which improved computational efficiency by reducing the dimensionality of the problem. This method achieved faster convergence but remained less effective in highly nonlinear systems. Later in the same year, [PERSON] et al. [24] presented a Genetic Algorithm-Based Multi-Objective Optimization, which allowed for the simultaneous optimization of multiple objectives, such as reducing error and computational time. While promising, this method introduced additional computational complexity and required careful management of trade-offs between objectives. The evolution of optimization algorithms for thermal model correlation reflects the growing complexity of spacecraft thermal systems. Deterministic methods, such as gradient-based and Quasi-Newton algorithms, are computationally efficient but struggle to handle nonlinear problems, often yielding suboptimal solutions. In contrast, stochastic methods, particularly Genetic Algorithms (GAs) [3] and Particle Swarm Optimization (PSO) [15], are more effective at navigating complex, nonlinear, and non-convex search spaces, finding global optima where deterministic methods fail. However, stochastic methods incur significant computational costs, necessitating careful parameter tuning to ensure convergence. Hybrid approaches, such as combining Multiple Linear Regression (MLR) with optimization algorithms, attempt to improve efficiency without sacrificing accuracy, though their effectiveness in highly nonlinear problems remains limited. In summary, while no single optimization method offers a comprehensive solution for spacecraft thermal model correlation, Genetic Algorithms have consistently proven to be among the most robust for handling these complex problems. With advances in computational power, hybrid methods and multi-objective optimization techniques are emerging as promising approaches, offering a balance between accuracy, efficiency, and flexibility.