## 5 Evaluation and Discussion
After a large number of tests, it was determined that these parameter values were the most appropriate to obtain the best possible results with the hybrid GA + ABO, GA, and ABO. No algorithm achieved better results with a larger number of iterations than 500, as well as with a larger number of solutions within the population, which was set to 15,000. Through a large number of experiments, the other parameter values in the table, which are important for the execution of the algorithms themselves, proven to be the best for solving this test set. The optimization results obtained with the algorithms GA, ABO, BSA and our new hybrid algorithm for the test set with 20 different sized CRPs are shown in Table 2.  
The numbers in bold indicate the best results for a given test set, taking into account the different bay sizes within the CRPs. Compared to the individual algorithms GA and ABO, our novel hybrid algorithm achieved the best result independently or shared the best result with GA or ABO. For the bay sizes \(5\times 5\), \(6\times 5\), \(4\times 6\), \(5\times 6\), \(6\times 6\), \(4\times 7\), \(5\times 7\), \(6\times 7\), our hybrid algorithm achieved the best results not sharing with ABO or GA. Thus, it can be concluded that this hybrid variant of the algorithm is very successful in solving the mentioned optimization problems and achieves better results than the individual variants of the algorithms. Thus, the combination of GA and ABO together with the introduction of the pseudorandom number generators SFMT-64 and SFMT-32 led to an improvement in the optimization results. It can also be seen that our hybrid algorithm and the individual algorithms GA and ABO give similar results for CRP with a smaller bay size. As soon as the size of the bay in CRPs increases (i.e., there is a larger number of containers in the bay and the problem becomes more complicated), it can be seen that the hybrid algorithm finds much better solutions than the individual algorithms GA and ABO. For CRPs with a larger bay size, the quality of the hybrid approach and the proposed adaptive switch method, which exploits the best features of GA and ABO together with better PRNGs, consequently leading to much better optimization results, comes into play. Furthermore, this can also be confirmed by examining the optimization results of the BSA algorithm, which to the best of our knowledge has so far produced the best results for this test data set. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of iterations & 500 \\ Population size & 15,000 \\ The maximum number of consecutive iterations in the single algorithm without improving best solution & 15 \\ Number of variables within the solution & 12 \(\times\) \#containers within particular CRP \\ The selection rate from previous iteration within GA & 0.75 \\ The rate of mutation in GA & 0.10 \\ The maximum number in ABO without improving the best solution & 5 \\ The value of factor lp1 in ABO & 0.5 \\ The value of factor lp2 in ABO & 0.3 \\ Ratio of use of SFMT-64 and SFMT-32 & 50/50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The setup of new hybrid metaheuristic algorithm GA + ABO, GA, and ABO for resolving CRPs. Our new hybrid algorithm achieved better overall results than the BSA algorithm. Except for the test instances where BSA and our hybrid algorithm performed equally well (\(4\times 3\), \(5\times 3\), \(6\times 3\), \(3\times 4\), \(3\times 5\), \(3\times 6\), \(3\times 7\)), the hybrid algorithm outperformed BSA on 8 test instances (\(3\times 3\), \(4\times 4\), \(4\times 5\), \(5\times 5\), \(6\times 5\), \(6\times 6\), \(4\times 7\), \(5\times 7\)), while BSA achieved better results on 5 test instances (\(5\times 4\), \(6\times 4\), \(4\times 6\), \(5\times 6\), \(6\times 7\)).