**6. Results and discussion**

This section is dedicated to describing and confirming which algorithms are the best in comparison. And the proposed multi-target bat computation (MaBAT/R2) with decay was implemented in Matlab, depending on the problem imposed. The proposed method has been tested with a variety of items, including community size (n), number of iterations, and rate of access reduction β.

The results were applied to fit the proposed methodology for balancing convergence and diversity. On the other hand, we compared MaBAT/R2 with two multitarget PSO accounts to get and know its severity and power to reach the optimal solution. MOPSO [13] and MOEA/D [10] are two different methods. Each calculation is repeated several times in order to achieve the metrics (IGD) and (HV) for each test work. **Table 1** show the following results:


*Using Many Objective Bat Algorithms for Solving Many Objective Nonlinear Functions DOI: http://dx.doi.org/10.5772/intechopen.107078*

#### **Table 1.**

*The mean and standard deviation of the IGD value of the proposed algorithms and the four recently comparative algorithms IBEA, BiGE, KnEA, RVEA, and MaBAT/R2 on DTLZ (1–5) for 5, 10, 15, and 20 objective problems, where the best value for each test case is highlighted with a bold background.*

**Figure 1.**

*Number of functions of VS fitness value graph for DTLZ1 and DTLZ2, such that N=No. of population, M = No. of objective function, D = dimension.*
