Conclusion
As a swarm-based algorithm, GTA exhibits good results in terms of consistency and speed to find the optimal solution for the benchmarking problems considered. GTA performance is clearly superior to PSO in terms of speed and consistency when functions with a huge (human genome-like) search space (up to 20,000 decision variables) were tested. The great advantage of GTA is its ability to direct the cyclists to search the fastest path for improvement of the objective function. At the same time, points with worst values for the objective functions are locally explored, since they can be just a barrier for a big improvement. The search for the fastest path has a similar goal as the first derivative, hence its fast convergence, despite the number of cyclists and iterations used. Finally, the sensitivity analysis showed the robustness of GTA, since the change in the main parameters had little impact in the final result. This feature results in a user-friendly algorithm, because there is no concern in adjusting the default parameters to perform a good optimization. Therefore, the three main aspects desirable for an optimization algorithm – ease of implementation, speed of convergence and reliability – had good results, confirming the expected improvements of GTA.