A Data-Driven Multi-Objective Evolutionary Algorithm Based on Combinatorial Parallel Infilling Criterion

摘要

Data-driven multi-objective evolutionary algorithm provides an effective way to solve multi-objective optimization problems with computationally expensive black-box functions. In this paper, a data-driven multi-objective evolutionary algorithm based on combinatorial parallel infilling (DDMOEA-CPI) is proposed. The DDMOEA-CPI uses the Kriging model in lieu of the real function, and combines the infilling criteria based on the multi-objective lower confidence bound (MLCB), as well as the maximum Kriging error of the Pareto front, in order to guide the population evolution to quickly obtain the accurate Pareto solutions. Within the criteria, the hyper volume improvement function is used to select new samples from the solutions of the MLCB. A set of benchmark tests in 20 dimensions are taken to validate and evaluate the performance of the DDMOEA-CPI. The results show that the proposed method behaves well in the convergence and diversity of Pareto solutions.