# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 6 Conclusions and Future Works  
In this work, we have first presented an algorithm that is a behavioral network made up by six different behaviors, whose parameters are optimized by a genetic algorithm and adapt to the scenario. Furthermore, based on the literature, six additional algorithms have been proposed, some of which have been combined to improve their efficiency. Additionally, three models to measure the efficiency are suggested, and a fitness function, which takes into account the robustness of the algorithm against the variability of the initial conditions. The algorithms have been compared making use of the models of the efficiency and the fitness for four scenario types. Finally, the communication complexity and the possibility of adapting the algorithms to surveillance and patrolling tasks have been analyzed, as well. All the algorithms compared fulfill robustness and scalability. Taking a look at the results, our opinion is that Models 1 and 2 to measure the efficiency do not represent correctly the performance of each algorithm. Note that if a cell is visited twice, the efficiency suffers a drop. However, subsequent visits to the same cell do not have an impact on the efficiency, although those visits could have been used potentially to visit new unvisited cells. This situation would affect the efficiency if Model 3 is considered, since the time to finish the search would increase. As has been already pointed out, the random movement achieves efficiencies of 61% with Model 1 for the plain scenario (whose performance is expected to be low a priori), whereas the best algorithm reaches 78%, which also indicates that the model may not be a good indicator of the performance. Therefore, we consider Model 3 as the one that more faithfully represents the efficiency. Comparing the efficiency and fitness values, we have seen that there are four algorithms that are competitive: behaviors set (A1), go to the closest unvisited cell (A3), travel along lanes (A7) and boundary following (A4). The other three achieve much lower values. In Table 5, a final comparison has been shown; for each scenario type, the efficiency and fitness have been divided by the maximum value achieved. As can be seen, A1's performance is the highest for all scenario types, although for the plain scenario and the scenario with the probability distribution, the difference is not high. In those cases, A3 and A7 could reach a good performance in terms of fitness. However, for scenarios with obstacles, the difference is higher. The second best algorithms, which are A3 and A4, achieve 87% and 84% of the maximum efficiency and fitness. Finally, if the scenario considered has obstacles and a probability distribution, A1 outperforms the other algorithm more significantly; again, the second best algorithm is A3, only reaching 76% and 77% of the maximum efficiency and fitness. If the task at hand is just searching in an area, probably the preferred algorithm would be separating the search space into lanes (A7) or that each agent heads to the closest non-visited cell (A3), because they are easy to implement, do not need any optimization process and perform well. However, if we want to make use of a map of probabilities or there are non-flyable obstacles in the area, algorithm A1 should be considered. Similarly, if the task is surveillance or patrolling, A1 may be also convenient, although this is only an intuition, and simulations need to be carried out to ground this assertion. In future works, the search patterns should be modified to adapt to all the scenario types, trying to achieve better results; this way, the comparison with A1 would be more fair. If in these modifications, parameters have to be included, an optimization process may need to be performed. In a similar way, the surveillance task may be analyzed with these algorithms (modifications will also need to be included, with the corresponding tuning).