global optimization, differential evolution, control parameters, self-adaptive algorithms

This paper is focused on the adaptation of control parameters in differential evolution. The competition of different control parameter settings was proposed in order to ensure the self-adaptation of parameters within the search process. Several variants of such algorithm were tested on six functions at four levels of search-space dimension. This competitive differential evolution proved to be more reliable and less time-consuming than the standard differential evolution. The competitive differential evolution also outperformed other algorithms that were tested.

This paper is focused on the adaptation of control parameters in differential evolution. The competition of different control parameter settings was proposed in order to ensure the self-adaptation of parameters within the search process. Several variants of such algorithm were tested on six functions at four levels of search-space dimension. This competitive differential evolution proved to be more reliable and less time-consuming than the standard differential evolution. The competitive differential evolution also outperformed other algorithms that were tested.