# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 2 Literature Review
### Implementing Heuristic Methods
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**N\({}^{\text{a}}\)** & **Algorithm** & **Year** & **Parameters Tuning** & **References** \\ \hline
1 & \
\begin{tabular}{l} Quasi-Newton algorithms (Broyden \\ class) [19] \\ \end{tabular} & 1965 &
\begin{tabular}{l} 1-Tolerance; \\ 2-Max iterations. \\ \end{tabular} & [17; 18; 32] \\ \hline
2 & Evolutionary Algorithms (EAs) [33] & 1966 &
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-Mutation rate; \\ 4-Mutation magnitude. \\ \end{tabular} & NA \\ \hline
3 & Evolutionary Strategies (ESs) [34] & 1973 &
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 4-Mutation rate; \\ 5-Mutation magnitude. \\ \end{tabular} & NA \\ \hline
4 & Genetic Algorithm (GA) [3] & 1975 & \begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-Crossover rate; \\ 4-Mutation rate; \\ 5-Mutation magnitude. \\ \end{tabular} & \
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-Crossover rate; \\ 4-Mutation rate; \\ 5-Mutation magnitude. \\ \end{tabular} &
\begin{tabular}{l} 21; 22; 35; 36; 37; 38] \\ \end{tabular} \\ \hline
5 & Multiple Linear Regression (MLR) [39] & 1981 &
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ \end{tabular} & [23] \\ \hline
6 & Simulated Annealing (SA) [31] & 1983 &
\begin{tabular}{l} 1-Initial temperature; \\ 2-Cooling rate; \\ 3-Minimum temperature; \\ 4-Max iterations. \\ \end{tabular} & [20; 40] \\ \hline
7 & Memetic Algorithms (MAs) [41] & 1989 &
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-Crossover rate; \\ 4-Mutation rate; \\ 5-Mutation magnitude. \\ \end{tabular} & NA \\ \hline
8 & Tabu Search (TS) [42] & 1989 &
\begin{tabular}{l} 1-Tabu tenure; \\ 2-Max iterations. \\ \end{tabular} & NA \\ \hline
9 & “Alienor” method [43] & 1989 &
\begin{tabular}{l} 1-N\({}^{\text{o}}\) iterations; \\ 2-Subdivision factor. \\ \end{tabular} & NA \\ \hline
10 & Noising method [44] & 1993 &
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) iterations; \\ 3-Noising rate; \\ 4-Noise magnitude. \\ \end{tabular} & NA \\ \hline
11 & Cultural Algorithms (CAs) [45] & 1994 &
\begin{tabular}{l} 1-Population size; \\ 2-Cultural population size; \\ 3-N\({}^{\text{o}}\) generations; \\ 4-Crossover rate; \\ 5-Mutation rate; \\ 6-Mutation magnitude. \\ \end{tabular} & NA \\ \hline
12 & Particle Swarm Optimization (PSO) [15] & 1995 & \
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-c1 (Cognitive coefficient); \\ 4-c2 (Social coefficient); \\ 5-w (Inertia weight). \\ \end{tabular} &
\begin{tabular}{l} NA \\ 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-Crossover rate; \\ 4-Mutation rate; \\ 5-Mutation magnitude. \\ \end{tabular} \\ \hline
13 & \
\begin{tabular}{l} Approximate Convex Underestimation \\ (ACU) [47] \\ \end{tabular} & 1995 &
\begin{tabular}{l} 1-Population size; \\ 2-N\({}^{\text{o}}\) generations; \\ 3-Crossover rate; \\ 4-Mutation rate; \\ 5-Mutation magnitude. \\ \end{tabular} & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of optimization algorithms, their parameters, and thermal modeling references.