global optimization, differential evolution, adaptation, parameter settings, optimization competition

Successful adaptive variant of differential evolution, the Success-history based parameter adaptation of Differential Evolution using linear population size reduction algorithm (L-SHADE), was improved. Adaptive mechanisms used in the algorithm were joined with adaptive mechanism proposed for competitive differential evolution algorithm. Four strategies, including the original one and strategies with exponential crossover, compete in the new LSHADE44 algorithm.The proposed algorithm is applied to the benchmark set defined for Learning-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 the most of the test problems.