TY - GEN
T1 - A Data-Driven Multi-Objective Evolutionary Algorithm Based on Combinatorial Parallel Infilling Criterion
AU - Li, Chunna
AU - Yang, Lianbo
AU - Gong, Chunlin
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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 multiobjective 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.
AB - 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 multiobjective 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.
KW - Data-driven
KW - Kriging model
KW - Multi-objective optimization
KW - Parallel infilling criterion
KW - Pareto solutions
UR - https://www.scopus.com/pages/publications/85124581198
U2 - 10.1109/CEC45853.2021.9504757
DO - 10.1109/CEC45853.2021.9504757
M3 - 会议稿件
AN - SCOPUS:85124581198
T3 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
SP - 909
EP - 916
BT - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021
Y2 - 28 June 2021 through 1 July 2021
ER -