## 6 Experimental Results
### Crowdsourcing the FLP in Video Game Plays  
The results obtained through the implementation of the genetic algorithms reveal the existence of gaps in the solution space. This is due to the lack of agreement between the two utilized genetic algorithms (MOPSDA and NGSA) in determining the optimal solution, as they both tend to converge toward local minima. The incompleteness of the solutions for  
Figure 6: Computed Pareto set for the London dataset using genetic algorithms for a large instance problem. various Pareto front instances is therefore considered as a drawback. To mitigate this issue, a re-execution of the genetic algorithm could be performed with the hope of obtaining solutions for the missing data, although there is no guarantee that the algorithm would be able to fill in the gaps in subsequent runs. To address the aforementioned problem, the FLP was modeled as a video game and crowdsourced to a number of individuals. The contributions obtained from the crowd were found to be adequate in filling in the gaps left by the genetic algorithms. The results of this approach are presented in the following figures. The FLP is a type of optimization problem that involves multiple objectives. The solution to this problem involves finding a set of solutions, called the Pareto set, which cannot be improved in one objective without negatively impacting another objective. To evaluate the quality of the solution, the aggregate data from players is measured based on how closely it approximates the Pareto set as determined by alternative methods. The optimal responses used in our analysis were selected from a dataset containing results from 20 different players, as illustrated in the accompanying Figure 7. The selection process involved choosing the most accurate and reliable responses, ensuring that our analysis was based on high-quality data. As can be observed from the figures, the results obtained through crowdsourcing--that is, through the engagement of individuals playing a video game modeling the FLP problem--are comparable to those obtained through sophisticated optimization tools such as genetic algorithms. Figure 7 demonstrate that gamers follow the trend in the Pareto front. When they play optimally, they effectively enhance the results obtained through genetic algorithms. Furthermore, the intuition and perspective of gamers enable them to make trade-offs between short-term benefits and long-term goals, thus contributing to better solutions. The biggest advantage of crowdsourcing can be observed in problems with a high number of facilities, as is the case for the London dataset. In this instance, the genetic algorithms generated significant gaps in the solution space, whereas crowdsourced solutions matched and even improved upon the solutions generated by genetic algorithms. Re-executing the genetic algorithms to fill these gaps would consume a substantial amount of computational resources, in addition to the time required for the genetic algorithms to generate the complete Pareto front. The re-run of the genetic algorithm for the London dataset is estimated to take approximately two to three hours, without any guarantee of filling the previously encountered gaps. Figure 7: The Pareto set after solving the FLP complemented via crowdsourcing for: the Swain dataset (**left**), and the London dataset (**right**). However, a limitation of the crowdsourcing approach is that it is challenging to objectively quantify the duration required to construct and design video games. While an adept individual possessing programming skills and familiarity with multi-objective optimization problems (MOPs) may complete the coding process within a few weeks, an unskilled individual could require several months. Despite this, after the game has been constructed and developed, it can be conveniently adapted for other relevant datasets and utilized as a simulation model capable of swiftly responding to emergency scenarios.