Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitati...

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Published in:2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) pp. 1 - 7
Main Authors: Xie, Lindong, Li, Genghui, Lin, Kangnian, Wang, Zhenkun
Format: Conference Proceeding
Language:English
Published: IEEE 22.09.2023
Subjects:
Benchmark testing
Complex systems
continuous and categorical variables
dual-state-driven
Evolutionary computation
expensive mixed-variable optimization
Measurement
Prediction algorithms
Predictive models
Search problems
Surrogate-assisted evolutionary algorithm
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Abstract The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems.
AbstractList The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems.
Author Lin, Kangnian
Xie, Lindong
Wang, Zhenkun
Li, Genghui
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  organization: School of System Design and Intelligent Manufacturing, Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China
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SubjectTerms Benchmark testing
Complex systems
continuous and categorical variables
dual-state-driven
Evolutionary computation
expensive mixed-variable optimization
Measurement
Prediction algorithms
Predictive models
Search problems
Surrogate-assisted evolutionary algorithm
Title Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables
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