Morphological filters for global optimization

This paper is devoted to construct an algorithm based on morphological filters, for global optimization of functions in big dimensions. A panel of various benchmark problems with different properties were used to assess the performance of the proposed Optimization morphological filters (OMF) algorithm. The obtained results has shown the scalability of the algorithm in contrast to optimization algorithms encountered in the literature. Moreover, in comparison with some metaheuristics (PSO, CuCkoo, Bat, QPSO...), the computational results revealed that the proposed algorithm is an effective and efficient optimization algorithm.