## 2 Literature Review
Through a screening search algorithm, the optimal shape was determined [37]. Dynamic simulations and the Taguchi method using an orthogonal table were used to optimize a rotary control head rubber core sealing's performance and fatigue life [38]. Support vector regression and random forest light-weight surrogate models were tuned to predict rubber suspension bushing stiffnesses for different load cases. The training dataset was selected using the DOE method based on 1D kernel density estimations, and the stiffnesses were calculated with finite element simulations [39]. Laboratory tests were performed with different axial loads on rubber bushes used in dynamic vibration absorbers and showed good agreement with the finite element analysis results. Thus, these methods were used to obtain a large number of samples for which the neural network surrogate model was trained in MATLAB to approximate the behavior of rubber bushes [40]. The cross-section of an automotive door sealing to reach a better door closing performance was optimized in [41]. The relation between the cross-section parameters and compression load deflection property was approximated with a neural network surrogate model. The efficiency of the genetic algorithm and particle swarm optimization methods was compared with the average of 50 runs. The different parameters of the genetic algorithm were tested on the neural network model. The found metamodel based optimum shape was compared with the finite element simulation results and showed a 7.9% relative error. [PERSON] and [PERSON] found the support vector regression model with cubic kernel function suitable to predict the new geometric construction of rubber bumpers [42; 43]. Limited information is known in advance about the objective functions determined for the industry-related shape optimization tasks, so this paper uses simulated annealing for the search process. Another advantage of simulated annealing is that the search can be restarted from a new candidate point if there is an analysis running issue, which is common due to large deformations. Numerous new variants of the SA algorithm have been implemented in the last years to optimize engineering design problems [44; 45; 46; 47]. In [48], two-dimensional structures subject to quasi-static loads were investigated using ANSYS, and the globally optimum shapes were obtained by the simulated annealing search algorithm. In [49], the shape optimization of a steel shear key using SA and Abaqus was run to enhance its cycle fatigue performance. Based on the aforementioned articles, the algorithms were able to find a good environment of the global optimum effectively if the algorithm parameters were preselected with a trial-and-error method that requires much human interaction. If there is sufficient time to run a simulated annealing algorithm, it will perform well with a slow cooling function and a high initial temperature, as shown by [PERSON] and [PERSON] [50]. However, a good solution has to be found by the searching algorithm in a short time when the cost function is calculated by expensive computer simulations. Therefore, numerous papers work on the parameter tuning of the simulated annealing algorithm considering the computational efficiency and accuracy [51; 52]. The required number of function callsand the convergence speed of the algorithm highly depend on the treatment strategy of the temperature parameter, the effect of which has been examined in numerous research papers [53; 54; 55]. The values of annealing parameters for a given cooling strategy provide an additional option to reduce the computational cost of the algorithm as investigated in [56]. In [57] the annealing parameters were tuned analytically, while in [58] automatic parameter tuning using a genetic algorithm was studied. The appropriate choice of step size and initial temperature parameters was investigated for a wind turbine placement optimization task in [59]. Using the adaptive step size against the constant, the results were significantly improved.