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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| Vydané v: | 2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) s. 1 - 7 |
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22.09.2023
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Lindong surname: Xie fullname: Xie, Lindong email: 12132679@mail.sustech.edu.cn organization: School of System Design and Intelligent Manufacturing, Southern University of Science and Technology,Shenzhen,P.R. China – sequence: 2 givenname: Genghui surname: Li fullname: Li, Genghui email: genghuili2-c@my.cityu.edu.hk organization: School of System Design and Intelligent Manufacturing, Southern University of Science and Technology,Shenzhen,P.R. China – sequence: 3 givenname: Kangnian surname: Lin fullname: Lin, Kangnian email: 12132672@mail.sustech.edu.cn organization: School of System Design and Intelligent Manufacturing, Southern University of Science and Technology,Shenzhen,P.R. China – sequence: 4 givenname: Zhenkun surname: Wang fullname: Wang, Zhenkun email: wangzhenkun90@gmail.com 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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