On self-adaptation in multioperator local search

Local searching (LS) has proven to be an efficient optimization technique in clustering applications when minimizing stochastic complexity. In this paper, we propose a method for organizing LS in this context - the adaptive multi-operator local search (AMOLS) - and compare its performance to the non-adaptive multi-operator LS (MOLS) method. Both of these methods use several different LS operators to solve problems. MOLS applies the operators randomly, whereas AMOLS adapts itself to favour those operators which manage to improve the results more frequently. We use a large database of binary vectors representing strains of bacteria belonging to the family Enterobacteriaceae and a binary image as our test materials. The results show the benefits of self-adaptation