Open Access

Helena Stegherr, Michael Heider, Jörg Hähner

• Analysing the behaviour of metaheuristics comprehensively and thereby enhancing explainability requires large empirical studies. However, the amount of data gathered in such experiments is often too large to be examined and evaluated visually. This necessitates establishing more efficient analysis procedures, but care has to be taken so that these do not obscure important information. This paper examines the suitability of clustering methods to assist in the characterisation of the behaviour of metaheuristics. The convergence behaviour is used as an example as its empirical analysis often requires looking at convergence curve plots, which is extremely tedious for large algorithmic datasets. We used the well-known K-Means clustering method and examined the results for different cluster sizes. Furthermore, we evaluated the clusters with respect to the characteristics they utilise and compared those with characteristics applied when a researcher inspects convergence curve plots. We foundAnalysing the behaviour of metaheuristics comprehensively and thereby enhancing explainability requires large empirical studies. However, the amount of data gathered in such experiments is often too large to be examined and evaluated visually. This necessitates establishing more efficient analysis procedures, but care has to be taken so that these do not obscure important information. This paper examines the suitability of clustering methods to assist in the characterisation of the behaviour of metaheuristics. The convergence behaviour is used as an example as its empirical analysis often requires looking at convergence curve plots, which is extremely tedious for large algorithmic datasets. We used the well-known K-Means clustering method and examined the results for different cluster sizes. Furthermore, we evaluated the clusters with respect to the characteristics they utilise and compared those with characteristics applied when a researcher inspects convergence curve plots. We found that clustering is a suitable technique to assist in the analysis of convergence behaviour, as the clusters strongly correspond to the grouping that would be done by a researcher, though the procedure still requires background knowledge to determine an adequate number of clusters. Overall, this enables us to inspect only few curves per cluster instead of all individual curves.…