TY - GEN
T1 - Expensive Many-Objective Optimization by Learning of the Strengthened Dominance Relation
AU - Shen, Jiangtao
AU - Wang, Peng
AU - Dong, Huachao
AU - Wang, Wenxin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Expensive many-objective optimization problems (EMaOPs) are common in the real world, whose objective values need to be calculated by time-consuming computational simulations or expensive experiments. For EMaOPs, guiding the optimization process by surrogate models is a popular method. In this paper, an FNN-assisted evolutionary algorithm is proposed for better solving EMaOPs. Concretely, a feedforward neural network (FNN) is constructed by learning the strengthened dominance relation (SDR) between two solutions. Then promising samples are generated by evolutionary search based on the constructed FNN. The proposed method is compared with four state-of-the-art peer algorithms on a set of benchmark problems. Experimental results demonstrate its superiority.
AB - Expensive many-objective optimization problems (EMaOPs) are common in the real world, whose objective values need to be calculated by time-consuming computational simulations or expensive experiments. For EMaOPs, guiding the optimization process by surrogate models is a popular method. In this paper, an FNN-assisted evolutionary algorithm is proposed for better solving EMaOPs. Concretely, a feedforward neural network (FNN) is constructed by learning the strengthened dominance relation (SDR) between two solutions. Then promising samples are generated by evolutionary search based on the constructed FNN. The proposed method is compared with four state-of-the-art peer algorithms on a set of benchmark problems. Experimental results demonstrate its superiority.
KW - evolutionary algorithm
KW - expensive optimization
KW - feedforward neural network
KW - many-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85174532150&partnerID=8YFLogxK
U2 - 10.1109/CEC53210.2023.10254133
DO - 10.1109/CEC53210.2023.10254133
M3 - 会议稿件
AN - SCOPUS:85174532150
T3 - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
BT - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
Y2 - 1 July 2023 through 5 July 2023
ER -