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

Lindong Xie1; Genghui Li1; Kangnian Lin1; Zhenkun Wang2

10.1109/DOCS60977.2023.10294894

2023

The 5th International Conference on Data-driven Optimization of Complex Systems

979-8-3503-9353-8

2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS)

1-7

22-24 Sept. 2023

Tianjin, China

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.

Surrogate-assisted Evolutionary Algorithm Expensive Mixed-variable Optimization Continuous And Categorical Variables Dual-state-driven

第一 ; 通讯

EI

20234815141006

Combinatorial optimization

Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

IEEE

1.School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, P.R. China
2.Department of Computer Science and Engineering, School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, P.R. China

Lindong Xie,Genghui Li,Kangnian Lin,et al. Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables[C],2023:1-7.