Global optimization algorithm based on self-organizing centroids

This paper presents a stochastic real-parameter optimization algorithm which is based on the paradigm of the self-organizing maps (SOM) and competitive neural networks. The proposed algorithm is population based and a mutation and a selection operators are defined in analogy to standard evolutionary algorithms (EAs). In the proposed scheme the individuals move in the search space following the dynamics of a modified version of the SOM, which is based on a discrete dynamical filter. The proposed approach tries to take advantage of the explorative power of the SOM, and defines a new search strategy which is based on a combination of a local task and a global task, using neighborhood interactions. The proposed algorithm performance is compared with standard and state of the art variants of differential evolution (DE) algorithm. Wilcoxon tests show that the porposed algorithm is competitive with DE, advantages and disadvantages are outlined.