## 5 Discussion  
In this paper, we presented a new optimization tool called Optimus, which implements jEDE algorithm, for GH. We tested the performance of Optimus using 20 benchmark problems and a design optimization problem against GA, PSO, RBFOpt. Experimental results show that jEDE outperforms these algorithms by finding better fitness values. There are several reasons to explain this fact. First, Optimus uses self-adaptive approach for producing control parameters. This gives an advantage to adapt the algorithm itself according to the nature of the problem. Secondly, Optimus presented significantly smaller fitness values than the other tools. This is related to the ensemble of mutation strategies. While the other algorithms are based on only one mutation operator, Optimus uses three mutation operators. During searching for a better solution, using more than one mutation strategy enlarges the search space. This increases the ability of searching near-optimal results. Thirdly, Optimus does not update number sliders, which corresponds to parameters, for each iteration. Instead, generated populations are directly connected to the geometry. Therefore, required computation time for one generation is less than the other algorithms. Finally, optimal values of some problems are outside of the initialization range (e.g., \(F_{7}\)). One may argue that this situation can be faced in architectural design  
Figure 14: Optimized results of design problem. Figure 15: Illustration of the best alternative (Optimus) result for design optimization problem. problems, as well. Generating chromosome values outside of the initialization range is possible in the Optimus, whereas it is not possible in the other tools. RBFOpt algorithm is recently compared with metaheuristics using several building optimization problems in [12; 13; 14; 32]. According to the results, RBFOpt found better solutions than metaheuristics. In these studies, termination criterion is defined as number of iterations during the evaluation of different algorithms. This gives an advantage for RBFOpt, due to ability to discover desirable solutions with small number of function evaluations. However, metaheuristics require more function evaluations to find the optimal solutions. In this study, we defined the termination criterion as run time for 30 min to make a fair comparison. Results show that RBFOpt requires more computation time to execute one function than metaheuristics. For this reason, while comparing these algorithms, rather than iterations and/or function evaluations, run time should be considered as termination criterion. Fitness functions based on simulations (e.g., energy and daylight) may require a huge amount of time for the convergence during the optimization. From the point of metaheuristics, usage of surrogate models [63] can be a solution to overcome this drawback. Because, surrogate models require less amount of function evaluation while approximating the fitness. By this way, researchers can consider many replications in a short run time. Furthermore, handling constraints is another important topic for architectural design optimization. Most of the real-world problems require design constraints that may restrict the design alternatives during the optimization process. There are many constraint handling methods that can be practically integrated to the optimization algorithms [64]. In the reviewed literature, only Nelder-Mead optimization tool provides a constraint handling method for the design optimization problems in the GH. To make a fair comparison with Galapagos, SilverEye, and Opossum, we considered penalty function as constraint to find the feasible solution in the design optimization problem. However, there are extremely hard constraints that can be tackled in the real-world problems e.g., equality constraints. Currently, this is not available in GH. In addition to the advantages of Optimus mentioned above, there are some limitations, as well.