Large-scale Multiobjective Optimization via Problem Decomposition and Reformulation

Li, Lianghao1; He, Cheng2; Cheng, Ran2; Pan, Linqiang1

10.1109/CEC45853.2021.9504820

2021

IEEE Congress on Evolutionary Computation (IEEE CEC)

978-1-7281-8394-7

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)

2149-2155

JUN 28-JUL 01, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Large-scale multiobjective optimization problems (LSMOPs) are challenging for existing approaches due to the complexity of objective functions and the massive volume of decision space. Some large-scale multiobjective evolutionary algorithms (LSMOEAs) have recently been proposed, which have shown their effectiveness in solving some benchmarks and real-world applications. They merely focus on handling the massive volume of decision space and ignore the complexity of LSMOPs in terms of objective functions. The complexity issue is also important since the complexity grows along with the increment in the number of decision variables. Our previous study proposed a framework to accelerate evolutionary large-scale multiobjective optimization via problem reformulation for handling largescale decision variables. Here, we investigate the effectiveness of LSMOF combined with decomposition-based MOEA (MOEA/D), aiming to handle the complexity of LSMOPs in both the decision and objective spaces. Specifically, MOEA/D is embedded in LSMOF via two different strategies, and the proposed algorithm is tested on various benchmark LSMOPs. Experimental results indicate the encouraging performance improvement benefited from the solution of the complexity issue in large-scale multi-objective optimization.

Multiobjective optimization problem decomposition large-scale optimization offspring generation problem reformulation

其他

英语

CPCI-S ; EI

National Natural Science Foundation of China[61772214,61903178,61906081,"U20A20306"]

Computer Science ; Engineering ; Mathematical & Computational Biology ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Mathematical & Computational Biology ; Operations Research & Management Science

WOS:000703866100271

20220711650655

Benchmarking ; Decision making ; Evolutionary algorithms

Management:912.2 ; Optimization Techniques:921.5

Web of Science

1.Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Informat Proc & Intelligent Control, Educ Minist China, Wuhan, Peoples R China
2.Southern Univ Sci & Technol, Guangdong Prov Key Lab Brain Inspired Intelligent, Dept Comp Sci & Engn, Shenzhen, Peoples R China

Li, Lianghao,He, Cheng,Cheng, Ran,et al. Large-scale Multiobjective Optimization via Problem Decomposition and Reformulation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:2149-2155.