Evaluating the performance of L-SHADE with competing strategies on CEC2014 single parameter-operator test suite

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. The 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 curried out on a different benchmark set, the proposed algorithm with competing strategies outperformed the original L-SHADE. However, the performance of the proposed algorithm on the benchmark set of Single parameter-operator set based case of Special Session and Competitions on Real-Parameter Single Objective Optimization on CEC2016 is not so much higher than the performance of the original L-SHADE algorithm.