## 5 Evaluation and Discussion
Thus, to be sure that GA + ABO solves the problems faster than GA and ABO, we performed the following test to determine how quickly all three algorithms achieve the same specified high-quality solution for CRP test instances with a bay size 6 \(\times\) 7. In this evaluation of the time required to retrieve a given optimization result, the solutions obtained from GA are set as the reference results that the algorithms must retrieve. Since ABO provided the worst optimization results and may not be able to retrieve the optimization reference results obtained by GA for some of the 40 test instances, we include it in this test and stop the evaluation for a particular test instance if the time to retrieve the best result exceeds 60 s. We did not use the results of ABO as reference results for this evaluation, because we believe that GA + ABO and GA would very quickly reach the solution obtained by ABO for this 6 \(\times\) 7 test set, so the difference between the algorithms would not be noticeable. The size of the population _PS_ remains the same (1000), while the number of iterations _ITN_ depends on the time at which each algorithm reaches the reference result for each of the individual 40 test instances. For each algorithm and for each of the 40 test instances, 10 tests were performed to determine the average retrieval time of the reference result for each algorithm for all 40 test instances. Table 7 shows the average time to obtain GA reference results with the hybrid GA + ABO, GA, and ABO for all 40 test instances of CRP with a bay size 6 \(\times\) 7. We can see how our hybrid algorithm outperforms GA and ABO in speed of convergence to the best solutions. ABO is very slow here, although it finds very good solutions for a large number of iterations, but here, we limited ourselves to obtaining GA reference results within 60 s for each of the 40 test instances. The hybrid algorithm achieved the best time for 39 out of 40 test instances of CRP with the size 6 \(\times\) 7. Moreover, the average time of all average times (4.1) of the hybrid algorithm is more than two times faster than the average time of the single GA algorithm (10.9). At this point, it should be noted that the difference in the performance of the algorithms would be even greater if the reference results for which the retrieval time is requested were set to the values achieved by our hybrid algorithm in tests using a combination of _ITN_ and _PS_ 500 \(\times\) 1000 values. Also, it is important to emphasize that the high-quality PRNGs SFMT-64 and SFMT-32 used in our hybrid algorithm have much higher temporal complexity than standard PRNGS like random() or rand() used in individual GA and ABO. However, by using SFMT-64 and SFMT-32 in the randomization phase, the best solutions were reached much earlier. It can be concluded that the quality of SFMT-64 and SFMT-32 exceeded the temporal complexity of these PRNGs and fully justified their inclusion in our hybrid algorithm. Moreover, after each iteration within the optimization phase, our hybrid algorithm must compute the quality of the obtained solutions from the previous iteration to determine which single algorithm to use in the next iteration. This should further slow down our algorithm. Considering that our algorithm is much faster than the single GA and ABO, it is proven that our hybrid algorithm takes advantage of the best features of GA and ABO in searching the solution space and obtains much better results in much less time using high-quality PRNGs. In conclusion, the scientific research of hybrid algorithms using the best features of individual algorithms is justified. Moreover, the use of better PRNGs leads to better randomization in the optimization process and thus to better solutions in less time.