With respect to the convergence trends described in Figure 2, it can be observed that the proposed CBFO is capable of testifying a very fast convergence and it can be superior to all other methods in dealing with F1, F2, F3, F4, F5, and F7.
It is observed that CBFO has attained the exact optimal solutions for 30-dimension problems F8 and F12 in all 30 runs.
According to the corresponding convergence trend recorded in Figure 3, the relative superiority of the proposed CBFO in settling F8, F11, and F12 test problems can be recognized.
The results in Table 10 reveal that the CBFO is the best algorithm and can outperform all other methods in dealing with F15 problems.
In order to investigate significant differences of obtained results for the CBFO over other competitors, the Wilcoxon rank-sum test  at 5% significance level was also employed in this paper.
Hence, it can be approved that the results of the CBFO are statistically improved compared to the other methods.
The results demonstrate that the utilized chaotic mapping strategy and Gaussian mutation in the CBFO technique have improved the efficacy of the classical BFO, in a significant manner.
As shown, each fold possessed a different parameter pair (k, m) since the parameters for each set of fold data were automatically determined via the CBFO method.
As shown, all four fitness curves of CBFO converged into a global optimum in fewer than 20 iterations.
Thus, it gets more chances to find the optimal neighborhood size and fuzzy strength values by the CBFO, which aided the FKNN classifier in more efficiently achieving the maximum classification performance.
Additionally, implementing the feature selection using CBFO strategy to further boost the performance of the proposed method is another future work.