# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 6 Conclusions  
So many works exist in the literature that clearly outline the nature of the role of the initial population in the overall performance of metaheuristic algorithms. However, despite the role that initialisation plays and the efforts put forward by researchers in this research area, to our knowledge, no comprehensive survey of articles on the subject area exists. Therefore, the present study presents a comprehensive survey of different approaches to improving performances of metaheuristic optimizers, using their initialisation scheme. We also show the publication trends for research in this area, and the number of citations. Finally, we provided a glossary of efforts that have been made to improve the performance of metaheuristic algorithms using their initialisation scheme. We also include the areas of application of these improvements for easy reference by metaheuristic research enthusiasts. The number of articles published to date in the repositories that were discussed earlier showed that the area which focuses on the initialisation of the population of metaheuristic algorithms is relatively uncharted. Many of these metaheuristic algorithms have been proposed; however, less effort has been made regarding their initialisation scheme. Most \begin{table}
\begin{tabular}{c c c c} \hline  & **BA** & **BOA** & **GWO** \\ \hline  & **Mean Rank** & **Mean Rank** & **Mean Rank** \\ \hline rand & 7.10 & 12.35 & 12.30 \\ betarnd(3,2) & 4.20 & 9.25 & 9.90 \\ betarnd(2.5,2.5) & 2.70 & 12.05 & 10.90 \\ unifrnd(0,1) & 8.00 & 9.65 & 11.20 \\ lognrd(0,0.5) & 17.70 & 10.60 & 9.50 \\ exprnd(0.5) & 10.00 & 12.40 & 13.10 \\ raylrnd(0.4) & 4.90 & 12.25 & 12.40 \\ wblrnd(1,1) & 16.20 & 10.90 & 12.30 \\ lhsdesign() & 7.90 & 12.90 & 12.20 \\ Sobol() & 5.70 & 12.65 & 11.20 \\ Test Statistics a  & & & \\ N & 10 & 10 & 10 \\ Chi-Square & 160.917 & 33.892 & 25.217 \\ df & 22 & 22 & 22 \\ Asymp. Sig. & 0.000 & 0.050 & 0.287 \\ \hline \end{tabular}
\end{table} 
Table 20: [PERSON]’s test for classical functions. researchers opt for the commonly used random number generator whose disadvantages have been significantly studied. The ease of implementation of the random number generator may have contributed to its use by researchers. On the one hand, the hybridisation of metaheuristic algorithms has yielded great results in the literature. Authors have had a great degree of success in using different initialisation schemes for the algorithms. We see a promising avenue whereby researchers can explore these high-performing initialisation schemes to assess their efficacy. The size of the population and the iteration number can be varied along with these schemes. This can help in increasing the performance of the algorithms. Our experiments demonstrate that for the classical functions under consideration, BA is sensitive to the initialisation schemes, whereas GWO and BOA are not. The sensitivity of the algorithms is also problem-dependent, meaning that some functions were insensitive to the initialisation scheme. The population size and number of iterations play a role in the performance of the algorithms. We discovered that BA performed better with larger population sizes. GWO and BOA performed better when the number of iterations was greater. This conclusion is heavily dependent on the dimension problem; however, we believe that good population diversity and number of iterations will most likely lead to optimal solutions. We also identified the need for an initialisation method for these algorithms that are best suited to the specific problem domain with statistical backing to yield an optimal solution for that set of problems. Unfortunately, most papers on meta-heuristics usually perform very little statistical validation, and if they do it is only on a single problem that the researchers describe. Benchmarking meta-heuristics with systematic and sound statistical techniques is usually lacking from many published works in the literature.