Differential evolution, mutation, Euclidean distance,population size, archive, benchmark set, optimisation,experimental comparison

A Differential Evolution (DE) algorithm with distance-based mutation-selection, population size reduction, and an optional external archive (DEDMNA) is proposed and tested on the CEC 2021 benchmark suite. The three well-known mutation variants are chosen in combination with one crossover for this model. The distances of three newly generated positions are computed to select the most proper position to evaluate. In the proposed algorithm, an efficient linear population-size reduction mechanism is applied. Moreover, an archive is employed to store older effective solutions. The provided results show that the proposed variant of DEDMNA is able to solve 64 out of 160 optimisation problems. Moreover, DEDMNA outperforms the efficient adaptive j2020 variant in 102 problems, and it is worse only in 15 problems out of 160. From the comparison of DEDMNA with five state-of-the-art DE algorithms, the superiority of DEDMNA is obvious.