Figure 3 – GTA Objective function evolution for the sixteen benchmarking functions
For comparison purposes, the same functions were tested once using PSO, with a higher value, namely 3,000, for the maximum number of allowed iterations, but with the same number of cyclists (particles), 500. Table 2 shows the results obtained. The global minimum was achieved only in three cases: Paraboloid, Shaffer f6 and Salomon. Moreover, a higher number of evaluations were necessary to achieve these results. In addition, it can be observed that in most cases there was no convergence, even with a higher number of iterations, reaching the maximum number of evaluations possible. The extremely high dimension of the problems, with most of them depending on 20,000 decision variables, is the major contributor for this behavior. Many more iterations and many more particles could have been used to reach (perhaps) an optimal solution; however, the increase in the computational effort would had been huge (or unaffordable).