An algorithm to identify clusters of solutions in multimodal optimisation

Abstract

Clustering can be used to identify groups of similar solutions I in Multimodal Optimisation. However, a poor clustering quality reduces the benefit of this application. The vast majority of clustering methods in literature operate by resorting to a priori assumptions about the data, such as the number of cluster or cluster radius. Clusters are forced to conform to these assumptions, which may not be valid for the considered population. The latter can have a huge negative impact on the clustering quality. In this paper, we apply a clustering method that does not require a priori knowledge. We demonstrate the effectiveness and efficiency of the method on real and synthetic data sets emulating solutions in Multimodal Optimisation problems. © Springer-Verlag 2004.