Clustering by Adaptive Local Search with Multiple Search Operators

摘要

Local Search (LS) has proven to be an efficient optimization technique in clustering applications and in the minimisation of stochastic complexity of a data set. In the present paper, we propose two ways of organizing LS in these contexts, the Multi-operator Local Search (MOLS) and the Adaptive Multi-Operator Local Search (AMOLS), and compare their performance of single operator (random swap) LS method and repeated GLA (Generalised Lloyd Algorithm).