## 2 Materials and Methods
### Choice of the Optimization Algorithm
#### 2.4.2 GOMORS Framework  
[PERSON] and [PERSON] [21] implemented the Gap-Optimized Multi-objective Optimization using Response Surfaces (GOMORS) framework. Indeed, GOMORS is a surrogate-assisted algorithm that uses the response surface method to estimate costly objective functions for the optimization with a GA. GOMORS updates the response surface model with the points evaluated in parallel. It identifies new, high-potential points in each iteration for the expensive data evaluation by selection rules based on the surrogate model. The following steps comprehensively describe the algorithm based on the original article.  
* Step 0--Define Algorithm inputs: Firstly, the maximum number of expensive function evaluations is selected. Furthermore, the gap parameter and the number of costly evaluations after each iteration are chosen.
* Step 1--Initial Evaluation Points Selection: The experimental design selects the initial set of points and expensively evaluates the selected points via simulation.  
Step 2--Iterative Improvement: The algorithm runs iteratively until the maximum number of expensive function evaluations (abort criteria) is reached. * Step 2.1--Fit/Update the Response Surface Models: The response surface models fit each objective for the expensively evaluated simulation points. * Step 2.2--Surrogate-Assisted Global Search: GOMORS uses an MOEA to minimize the objective functions represented by the response surface models, selecting a set of potential candidates. * Step 2.3a--Identify Least Crowded Solution: The least crowded design is selected by calculating the crowding distance of the expensively computed data. * Step 2.3b--Local Search: The framework uses the least crowded solution as input for an MOEA-based neighborhood search, based on the response surface models, called the \"Gap Optimization Problem\". It processes the search in the radius of the initially defined gap parameter around the least crowded solution. * Step 2.4--Select Points for Expensive Function Evaluation: A candidate selection is processed based on selection rules, the data sets from the surrogate-assisted global search, and the local search. * Step 2.5--Perform expensive function evaluations and update the non-dominated solution set: The method evaluates candidate points by costly simulations and updates the response surface model with the data of the candidate points. * Step 3--Return Best Approximated Front: After the final loop, when the maximum number of expensive function evaluations is reached, the best non-dominated frontier is returned. For further details on GOMORS, the reader is referred to [21]. One problem with the current version of the GOMORS framework is that it is an implementation in Python 2.7, incompatible with Python 3.7. Hence, the GOMORS framework was ported to Python 3.7 to run in the existing IKOS environment. The article provides this modified GOMORS-code via GitHub [37]. Mainly, it consists of the following changes compared to the implementation from [21]: * It uses a symmetric Latin hypercube sampling instead of a Latin hypercube sampling; * The MOEA of choice is the epsilon-NSGA-II following Kollat and Reed; * Instead of the hypervolume improvement, the epsilon-progress-based selection is used.