## 6 Discussion and Conclusions  
In this paper, we applied the self-adaptation concept to develop an algorithm to improve the performance in finding solutions for the DGRP formulated in the previous study. The self-adaptation mechanism used in this paper attempts to identify a better strategy that can be selected in the future as the strategy for mutation with a higher probability. To identify a better strategy and the probability for serving as a mutation strategy in the future, the algorithm records the number of \"success events\" and the number of \"failure events\" in a learning period. The probability for serving as the mutation strategy is calculated based on the number of \"success events\" and the number of \"failure events\" in a learning period for each mutation strategy. A mutation strategy with a higher probability for serving as the mutation strategy will be selected with a higher probability. A mutation strategy with a lower probability for serving as the mutation strategy will be selected with a lower probability. In this way, the performance of the solution that is found can be improved more efficiently in terms of the average number of generations for most cases. However, due to the additional computation in each iteration, the computation time of SaNSDE-1-6 is much longer for big cases. A mutation strategy with a higher probability for serving as the mutation strategy indicates that the ratio between the number of \"success events\" and the total number of \"success events\" and \"failure events\" is higher. It is expected that using a mutation strategy with a higher probability for serving as the mutation strategy tends to improve the performance of the solution that is found. The results presented in the previous section confirm that using a more effective mutation strategy with a higher probability for serving as the mutation strategy indeed improves the performance of the solution that is found significantly. The degree of improvement is case dependent. With \(NP=30\), for Case 12, the improvement achieved using SaNSDE-1-6 is about 1% to 2%. For Case 13, the improvement achieved using SaNSDE-1-6 is about 5% to 10%. For Case 14, the improvement achieved using SaNSDE-1-6 is about 10% to 28%. In short, SaNSDE-1-6 outperforms NSDE, DE-1 and DE-3 in terms of scalability. With \(NP=50\), for Case 12, the improvement achieved using SaNSDE-1-6 is about 0.1674% to 0.73959%. For Case 13, the improvement achieved using SaNSDE-1-6 is about 4.00326% to 7.9796%. For Case 14, the improvement achieved using SaNSDE-1-6 is about 3.1171% to 46.403%. In short, SaNSDE-1-6 outperforms NSDE, DE-1 and DE-3 in terms of scalability. The bigger the problem size, the more significant the improvement. In the real world, when one person fails to solve a problem alone, it might be easier to solve the problem by asking another person for help and working together. The reason is that one may consult the other and/or help each other when taking actions or making decisions. This way to solve a problem effectively is commonly used in our daily life. The results of the experiments presented in this paper are consistent with the abovementioned phenomena in the real world. In our self-adaptation mechanism, there are two strategies involved in the solution-finding processes. The selection of one strategy in the solution-searching processes is based on the success probability learned from the learning period. To verify the effectiveness of the self-adaptation mechanism, we carried out experiments by applying several standard algorithms and our proposed algorithm. Two different population sizes were used to perform the experiments. We compared the effectiveness of several single strategy algorithms and the self-adaptation-based algorithm. Our results indicate that the proposed algorithm based on the self-adaptation mechanism improves the performance and convergence rate in terms of the average number of generations required