## 5 Evaluation and Discussion
Therefore, for CRP problems with a smaller number of columns and rows, it is not necessary to run metaheuristic algorithms with a large number of iterations and a large population size of each iteration (as in the evaluation section in Table 2). The first significant differences in results are observed for test instances of size 6 (rows) \(\times\) 4 (columns), where our hybrid algorithm immediately converges to the best possible solution even for the combination of _ITN_ and _PS_ 100 \(\times\) 100, while GA and ABO converge to better solutions (not the best) only when the parameters of _ITN_ and _PS_ are higher. Although this table shows test instances with a smaller number of containers, there is little indication of how the result turns out for larger test instances of CRP, where our new hybrid algorithm achieved the best results. The average test results of our new hybrid algorithm GA + ABO and the individual algorithms GA and ABO, for column (stacks) size 5 and all possible row (tier) sizes from 3 to 6 with different combinations of the number of iterations _ITN_ of the individual algorithm along with the population size _PS_ of each iteration can be seen in Table 4.  
Here we can already see how our new hybrid algorithm with the pseudorandom number generators SFMT-64 and SFMT-32 achieves very good optimization results for these test instances in fewer iterations and with a smaller population size within an iteration. For the test instances of 3 \(\times\) 5 and 4 \(\times\) 5, the difference between the hybrid algorithm and the individual algorithms GA and ABO is not so large, but for the sizes of CRPs of 5 \(\times\) 5 and 6 \(\times\) 5, the quality of our new hybrid solution can be observed. For the size of 5 \(\times\) 5, our hybrid algorithm already converges to the best solution when combining _ITN_ and _PS_ 100 \(\times\) 100, while GA and ABO do not reach the best solution even when combining _ITN_ and _PS_ 500 \(\times\) 1000. In this way, the hybrid GA + ABO can save computer resources, shorten the algorithm execution time and consequently reduce power consumption, which is one of the main tasks in all industries including computer science nowadays. Moreover, our hybrid algorithm proven to be the best even with a CRP size of 6 \(\times\) 5. It very quickly reached solutions that were close to the best solution (reached in Table 2), while the individual algorithms GA and ABO converged more slowly to better solutions. This difference in convergence is better seen in Figure 3, where the graph shows the average test results of our new hybrid algorithm GA + ABO and the individual algorithms GA and ABO for a CRP size of 6 \(\times\) 5. There you can see a significant difference in the results obtained for different combinations of _ITN_ and _PS_. The optimization results for the single algorithms GA and ABO decrease slightly as _ITN_ and _PS_ increase. The same happens with the hybrid GA + ABO, except that our algorithm converges very quickly to very good solutions for these test instances, which is already visible for the combination of _ITN_ and _PS_ of 100 \(\times\) 100. For CRPs with 6 columns, the difference in optimization results is even larger. Table 5 shows the average test results of our new hybrid algorithm and the individual algorithms GA and ABO, for columns (stacks) of size 6 for tests with different combination values of the _ITN_ and the _PS_ of each iteration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GA + ABO** & **GA** & **ABO** & & **GA + ABO** & **GA** & **ABO** \\ \  
For a CRP with a size of 3 \(\times\) 6, there is no difference in the optimization results but already for a CRP with a size of 4 \(\times\) 6, it can be seen that our hybrid algorithm reaches its best possible result already in the tests with the combination values of _ITN_ and _PS_ 100 \(\times\) 100, while the individual GA reaches its best possible result only in the tests with the combination values of _ITN_ and _PS_ 500 \(\times\) 1000.