## 6 Conclusions and Future Studies  
Optimization plays an important role in science and technology today, as it aims to find the best solution to problems. In computer science, it is particularly important to develop algorithms that produce high-quality results with as few resources as possible. Real-world optimization problems, which are often NP-hard, benefit from the use of metaheuristics because they provide computationally efficient solutions to complex challenges. Today, metaheuristic algorithms are well established in computer science and mathematical optimization, providing faster and computationally efficient solutions. However, using only one metaheuristic algorithm to solve a particular optimization problem sometimes leads to premature convergence and local optima and can reduce the quality of the optimization. To overcome this limitation, hybrid metaheuristic algorithms are developed by combining multiple metaheuristic algorithms to exploit their respective strengths and mitigate weaknesses. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Instance** & **GA + ABO** & **GA** & **ABO** \\ \hline
1 & **6.3** & 22.0 & N/A \\
2 & **1.7** & 5.0 & 45.3 \\
3 & **3.3** & 6.3 & N/A \\
4 & **5.0** & 18.0 & 49.2 \\  
\hline \hline Average time & **4.1** & 10.9 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 7: 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 \(6\times 7\). In this paper, a new hybrid nature-inspired metaheuristic algorithm based on GA and ABO is proposed for optimizing real-world problems. The dynamic switching mechanism between GA and ABO within the optimization process allows the algorithm to adapt to different problem characteristics and improve the overall performance. In contrast to the traditional sequential execution, an adaptive switch is incorporated into the hybrid algorithm that dynamically selects either GA or ABO based on their performance in the previous iteration. This innovative adaptive approach contributes to higher efficiency and robustness in solving complex optimization problems. Moreover, this hybrid algorithm can be easily modified to base the optimization process on two other metaheuristic algorithms. Moreover, one of the features of this new hybrid algorithm is the use of selected PRNGs in the randomization process. To improve the performance of the hybrid algorithm, two high quality PRNGs, SFMT-64 and SFMT-32, were used, which had a significant impact on the ability of the algorithm to avoid premature convergence and local optima, leading to better optimization results. The hybrid algorithm was evaluated on the NP-hard CRP problem, focusing on a restricted CRP due to its higher complexity. The experimental results show the effectiveness of the proposed hybrid solution, outperforming the individual algorithms GA and ABO, which use standard PRNGs. The hybrid algorithm also achieves the best optimization results for the tested CRPs and outperforms state-of-the-art optimization methods. In the future, it would be interesting to test this new hybrid algorithm with some other individual nature-inspired metaheuristic algorithms, not only GA and ABO built into the hybrid algorithm. In this way, for any optimization problem in engineering, one could find a suitable pair of algorithms whose properties correspond to the optimization problem to be solved. Moreover, this hybrid algorithm could be extended to three or more algorithms which activation within the hybrid algorithm is implemented by a dynamic switching mechanism as in this work. Moreover, the dynamic switching mechanism could be improved by introducing some new performance methods of the individual algorithm that evaluate how well the currently active individual algorithm has performed in previous iterations.