Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis

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-scal...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 28; H. 1; S. 1
Hauptverfasser:	He, Cheng, Cheng, Ran, Li, Lianghao, Tan, Kay Chen, Jin, Yaochu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Convergence Decision analysis decision variable analysis Effectiveness evolutionary algorithm Evolutionary algorithms Iron Large-scale optimization Maintenance engineering Multiple objective analysis Optimization problem reformulation Sociology Statistics Variables Visualization
ISSN:	1089-778X, 1941-0026
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Abstract	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.
AbstractList	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.
Author	Tan, Kay Chen Li, Lianghao Cheng, Ran Jin, Yaochu He, Cheng
Author_xml	– sequence: 1 givenname: Cheng orcidid: 0000-0003-4218-8454 surname: He fullname: He, Cheng organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China – sequence: 2 givenname: Ran orcidid: 0000-0001-9410-8263 surname: Cheng fullname: Cheng, Ran organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China – sequence: 3 givenname: Lianghao orcidid: 0000-0002-5810-8749 surname: Li fullname: Li, Lianghao organization: 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 – sequence: 4 givenname: Kay Chen orcidid: 0000-0002-6802-2463 surname: Tan fullname: Tan, Kay Chen organization: Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR – sequence: 5 givenname: Yaochu orcidid: 0000-0003-1100-0631 surname: Jin fullname: Jin, Yaochu organization: Chair of Nature Inspired Computing and Engineering, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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SubjectTerms	Convergence Decision analysis decision variable analysis Effectiveness evolutionary algorithm Evolutionary algorithms Iron Large-scale optimization Maintenance engineering Multiple objective analysis Optimization problem reformulation Sociology Statistics Variables Visualization
Title	Large-scale Multiobjective Optimization via Reformulated Decision Variable Analysis
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