Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis

Cheng He1; Ran Cheng2; Lianghao Li3; Kay Chen Tan4; Yaochu Jin5

2022

10.1109/TEVC.2022.3213006

IEEE Transactions on Evolutionary Computation   影响因子和分区

1941-0026

1941-0026

With the rising number of large-scale multiobjective optimization problems (LSMOPs) from academia and industries, some multiobjective evolutionary algorithms (MOEAs) with different decision variable handling strategies have been proposed. Decision variable analysis (DVA) is widely used in large-scale optimization, aiming at identifying the connection between each decision variable and the objectives, and grouping those interacting decision variables to reduce the complexity of LSMOPs. Despite their effectiveness, existing DVA techniques require the unbearable cost of function evaluations for solving LSMOPs. We propose a reformulation-based approach for efficient DVA to address this deficiency. Then a large-scale MOEA is proposed based on reformulated DVA, namely, LERD. Specifically, the DVA process is reformulated into an optimization problem with binary decision variables, aiming to approximate different grouping results. Afterwards, each group of decision variables is used for convergence-related or diversity-related optimization. The effectiveness and efficiency of the reformulation-based DVA are validated by replacing the corresponding DVA techniques in two large-scale MOEAs. Experiments in comparison with six state-of-the-art large-scale MOEAs on LSMOPs with up to 2000 decision variables have shown the promising performance of LERD.

Large-scale optimization evolutionary algorithm decision variable analysis problem reformulation

SCI ; EI

英语

其他

COMPUTER SCIENCE

2-s2.0-85139834297

IEEE

1.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China
2.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
3.School of Artificial Intelligence and Automation, Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan, China
4.Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
5.Department of Computer Science, University of Surrey, Guildford, U.K.

Cheng He,Ran Cheng,Lianghao Li,et al. Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis[J]. IEEE Transactions on Evolutionary Computation,2022,PP(99):1-1.

Cheng He,Ran Cheng,Lianghao Li,Kay Chen Tan,&Yaochu Jin.(2022).Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis.IEEE Transactions on Evolutionary Computation,PP(99),1-1.

Cheng He,et al."Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis".IEEE Transactions on Evolutionary Computation PP.99(2022):1-1.