## 5 Evaluation and Discussion
The individual ABO does not reach its best result of 12.05, even in the tests with the combination of _ITN_ and _PS_ 500 \(\times\) 1000. The biggest difference in convergence to the best optimization results is seen in CRP with a size of 6 \(\times\) 6, which graphical representation can be seen in Figure 4. For the tests where the values of _ITN_ and _PS_ are smaller, a significant difference in convergence to better solutions can be seen. In the tests with _ITN_ and _PS_ 500 \(\times\) 1000, it can be seen that GA converges to GA + ABO, while the individual results of ABO are much weaker. Again, this shows that the new hybrid algorithm can achieve very good solutions in less time and with fewer resources, while reducing the power consumption to solve this optimization problem. The final comparison of optimization results of our new algorithm with individual GA and ABO was performed for CRPs with seven columns, which is the most complex set of test instances of CRPs for realistic bay sizes in a port container terminal. This is where the difference in optimization results is greatest for the algorithms compared. Table 6 shows the average test results of our new hybrid algorithm and the individual algorithms GA and ABO for columns (stacks) of size 7 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  & **GA + ABO** & **GA** & **ABO** & **GA + ABO** & **GA** & **ABO** \\ \hline _ITN_\(\times\)_PS_ & & 3 (row) \(\times\) 6 (col) & & _ITN_\(\times\)_PS_ & & 5 (row) \(\times\) 6 (col) & \\ \hline
100  
In this table, one can already see the difference in the results. In this most complex CRP, it can be seen that our hybrid algorithm achieves better results when the values of _ITN_ and _PS_ increase. In this test instance, no algorithm immediately achieves anywhere near the best results. However, a large difference in convergence to better solutions can be seen between all three algorithms. Again, the hybrid algorithm achieves the best results, followed by the individual GA and then ABO. In addition, Figure 6 shows a comparison of the optimization results (the number of additional relocations) of the algorithms GA + ABO, GA, and ABO based on each of the 40 test instances of the large CRP test set with size \(6\times 7\) for the parameter values _ITN_ and _PS_ 500 \(\times\) 1000. In this figure, you can see the difference in the results of the compared algorithms for individual test instances that have different arrangements of containers within the bay. For some instances, all three algorithms give the same results, but in most instances, it can be seen that hybrid GA + ABO gives the best results (the smallest number of additional relocations). It is also interesting to compare the times required for the compared algorithms to achieve the same optimization result for each of the 40 test instances. This time comparison gives a clear picture of how much faster GA + ABO finds very good solutions for a given CRP. For the tests from Table 2, we did not report the execution time because we set the  
Figure 5: Average test results of the hybrid algorithm GA + ABO and the individual algorithms GA and ABO for a CRP size of \(6\times 7\) with different combinations of the number of iterations ITN and population size PS. Figure 6: A comparison of the optimization results (the number of additional relocations) of the GA + ABO, GA, and ABO for all 40 instances of the CRP test set with size of \(6\times 7\) for tests with combination values of ITN and PS 500 \(\times\) 1000.  
maximum parameter values (Table 1) so that the results were as good as possible for all three algorithms, but the execution time was very slow for all of them. However, our hybrid algorithm achieved its best solutions much faster (before reaching the maximum number of iterations) than any of GA and ABO during this test. Therefore, the timing data for tests in Table 2 is not relevant for comparing these three algorithms.