Self-adaptive Differential Evolution Algorithm with Population Size Reduction for Single Objective Bound-Constrained Optimization: Algorithm j21

In this paper, we propose a new algorithm for solving real parameter single-objective optimization problems that were prepared for the CEC 2021 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization. Single-objective optimization problems are often very complex and computationally expensive. The presented algorithm, called j21, uses several mechanisms: two populations, vectors are chosen from both sub-populations in the mutation operation, crowding in the big population, population size reduction, etc. We show the experimental results for each benchmark function for two scenarios of different dimensions and eight configuration scenarios as required by the organizers of the CEC 2021 Special Session. We also compare the obtained results of j21 in a scenario with larger dimension and on one selected configuration with the original DE and j2020 algorithms.