global optimization, differential evolution, adaptation, parameter setting

A new variant of differential evolution algorithm is proposed. The new variant is a modification of the success-history based parameter adaptation of differential evolution using linear population size reduction (L-SHADE). In the newly proposed variant, adaptive mechanism of competing strategies is added. Four different strategies combining two kinds of mutation and two types of crossover compete in generating the new trial points and selection of the strategy to be used in the current step is based on the success in previous search steps. The proposed algorithm is applied to the benchmark set defined for Single parameter-operator set based case of Special Session and Competitions on Real-Parameter Single Objective Optimization on CEC2016. According to preliminary experiments, the proposed algorithm with competing strategies outperformed the original L-SHADE in most of the test problems.