# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
## 7 Simulation Results
Figure 15: Evaluation of the proposed approaches based on V-shaped TF in terms of AFV. As shown in Figures 13 and 16, the ACA results show the superiority of IBGWO4-V compared with other approaches. It achieved the best ACA for six datasets, with five of them having an ACA equal to 1. IBGWO4-V achieved an average ACA value of 0.95 across all datasets. Moreover, IBGWO1-V obtained the best results for four datasets. In general, it was expected that IBGWO4-V would outperform other approaches since it considered in each iteration the ranking of the population that resulted from the previous iteration, the population that resulted from applying BGWO, and the population that resulted from both BGWO and BPSO. It was expected that IBGWO-2-V and IBGWO3-V would outperform IBGWO1-V; however, both were negatively affected by applying the V-shaped TF when compared with either BGWO-V or IBGWO4-V. This was not the case when using the S-shaped TF. Figure 14 shows that IBGWO4-V achieved the best, i.e., lowest, ASF results when compared with other approaches. Also, BGWO-V and IBGWO1-V, respectively, obtained the best results after IBGWO4-V. The same reasons apply as described previously when discussing Figure 8:0  
Figure 16 confirms the results obtained in Figures 14 and 15. IBGWO4-V achieved an average AFV value of 0.05 across all datasets. This confirms that the IBGWO4-V had the best ability over all other proposed approaches to scan the search space and achieve the global optimal solution without getting stuck in local optima. Figure 17 shows the evaluation results of the proposed FS optimizers based on the V-shaped TF in terms of the ACT. This figure confirms the results obtained previously in Figure 10, which shows that using either S-shaped or V-shaped TFs did not affect the computational time of the proposed approaches. Figures 18 and 19 show the convergence curves for all FS optimizers based on the V-shaped TF over all iterations. The two figures show that, unlike many optimizers that get stuck at local optima in early iterations, IBGWO4-V could converge to the optimal solution. These curves show that IBGWO4-V explored the search space before exploiting the optimal solution. In summary, the results shown in Figures 6-19 confirm the importance of selecting a suitable TF to increase the performance of the optimizers. Moreover, the good performance of the optimizer means a strong searching ability to avoid being stuck in a local optimum by having a good balance between global and local searching. Overall, IBGWO3-S and IBGWO4-V were the fittest optimizers based on S-shaped and V-shaped TFs, respectively. Figure 8:1 Evaluation of the proposed approaches based on V-shaped TF in terms of ACT. Figure 8:2 Convergence curves for all approaches based on V-shaped TFs (Part 1 of the datasets). * Comparison with state-of-the-art metaheuristics  
As confirmed in the previous two sections, IBGWO3-S and IBGWO4-V outperformed other approaches based on S-shaped and V-shaped TFs, respectively. This subsection presents the comparisons between these two approaches with eight state-of-the-art metaheuristic-based FS optimizers. The metaheuristics used in the comparisons were as follows: Binary Whale Optimization Algorithm (BWOA) [31], Binary Dragonfly Algorithm (BDA) [33], Binary Ant Lion Optimization (BALO) [36; 38], Binary Gravitational Search Algorithm (BGSA) [26], Binary Teaching-Learning-Based Optimization (BTLBO) [27], Binary Harris Hawk Optimization (BHHO) [32], Binary Bat Algorithm (BBA) [77], and Binary Salp Swarm Algorithm (BSSA) [78]. The work proposed in this paper was compared with the selected set of state-of-the-art feature selection optimizers since these optimizers were applied before to address our same research objective, which was to optimize the feature selection process. The next section will show the results of the comparison of our proposed approaches with one of the recent and advanced optimizers that combines binary PSO and binary GWO with a tournament selection strategy.