single-objective optimization, differential evolution, adaptation

A variant of adaptive differential evolution (DE) with twelve competing strategies was applied to the set of benchmark problems proposed for the CEC 2013 competition. Competitive adaptation of DE strategies and parameter settings avoids tuning of most control-parameter values because only the size of population must be set by the user of the algorithm. The experimental results showed acceptable reliability of the search first of all in the problems with unrotated objective functions of smaller dimension. The possible cause of unreliability of the algorithm in the rotated functions is analyzed.