A Grey Prediction-Based Reproduction Strategy for Many-Objective Evolutionary Algorithm

Abstract

Many-objective evolutionary algorithms (MaOEAs) consisted of environmental selection and reproduction operator. However, few studies focus on how to design reproduction operators to improve the performance of MaOEAs. In this paper, a reproduction operator based on grey prediction is proposed for MaOEAs, named GPRS. Specifically, the grey prediction assisted by reference vector is first used to get the target location. Then, a fine regulation is designed to generate potential solutions by handling the different information further. Finally, a gene sharing strategy is executed to accelerate the convergence by information exchange. The effectiveness of the proposed reproduction strategy is validated by comparing it with five widely used reproduction operators by embedding into a classical framework NSGAIII. At the same time, an improved NSGAIIIGPRS is developed by embedding the proposed GPRS and compared with seven excellent algorithms on a number of benchmark problems and one practical application. The final experimental results show that the proposed GPRS has significant advantages over similar reproduction strategies, and the improved NSGAIIGRPS is more effective compared with other excellent algorithms in handling many-objective optimization problem.