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.