Improving evolutionary algorithms with information feedback model for large-scale many-objective optimization

Recently, many evolutionary algorithms have been proposed. Compared to other algorithms, the core of the many-objective evolutionary algorithm using a one-by-one selection strategy is to select offspring one by one in environmental selection. However, it does not perform well in resolving large-scale many-objective optimization problems. In addition, a large amount of meaningful information in the population of the previous iteration is not retained. The information feedback model is an effective strategy to reuse the information from previous populations and integrate it into the update process of the offspring. Based on the original algorithm, this paper proposes a series of many-objective evolutionary algorithms, including six new algorithms. Experiments were carried out in three different aspects. Using the same nine benchmark problems, we compared the original algorithm with six new algorithms. Algorithms with excellent performance were selected and compared with the latest studies using the information feedback model from two aspects. Then, the best one was selected for comparison with six state-of-the-art many-objective evolutionary algorithms. Additionally, non-parametric statistical tests were conducted to evaluate the different algorithms. The comparison, with up to 15 objectives and 1500 decision variables, showed that the proposed algorithm achieved the best performance, indicating its strong competitiveness.