This research paper aims to explore the possibilities of parameterization of state-of-the-art adaptive mechanisms incorporated in the self-organizing migrating algorithm (SOMA). This algorithm has gained renewed interest from the research community while the algorithm's internal dynamics, mechanisms, and dependencies of the functionality on parameter settings have not been thoroughly examined using a data-driven approach. Our extensive test workflow has yielded valuable insights into the performance of the SOMA and its sensitivity to parameter settings that affect the migration of individuals. Important findings were also obtained regarding the appropriate parameter settings required for more complex techniques such as clustering, organization, and population restart. The research also highlighted the influence of different modern adaptation techniques and the suitability of combining different modern adaptation mechanisms, leading to the modular design of different configurations. The simulation experiments conducted provided new insights that could be useful for further research, especially with the rapid development in automatic configurators of meta-heuristic algorithms, modular concepts of algorithms, and their performance prediction.