Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization

Wang, Zhenkun1,2; Chen, Yuanyao1; Li, Genghui3; Xie, Lindong1; Zhang, Yu1

2024-12-01

10.1016/j.swevo.2024.101700

SWARM AND EVOLUTIONARY COMPUTATION   影响因子和分区

2210-6502

2210-6510

The efficacy of surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) in addressing expensive multi-objective optimization problems (MOPs) is contingent upon the modeling techniques and model-based infill sampling strategies. In addressing this pivotal aspect, this paper introduces a pioneering methodology known as batch subproblem coevolution with Gaussian process-driven linear models (BSCo-GPLM). Specifically, from a modeling perspective, BSCo-GPLM decomposes the MOP into single-objective subproblems. Following this decomposition, for each subproblem, a Gaussian process-driven linear model (GPLM) is collaboratively trained to prevent overfitting and improve prediction accuracy. Regarding infill sampling, collaborative optimization of all GPLMs yields optimal candidate solutions for each subproblem, organized into coherent clusters. Within each cluster, only the solution with the highest utility is evaluated. Relying on the heightened prediction accuracy of the GPLM model and an efficient batch sampling strategy, BSCo-GPLM exhibits clear superiority over state-of-the-art SAMOEAs in effectively addressing expensive MOPs. The source code of BSCo-GPLM is available at https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/BSCoGPLM.

Surrogate-assisted evolutionary optimization Gaussian process-driven linear model Batch subproblem coevolution Expensive multi-objective optimization

SCI ; EI

英语

第一

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:001301140800001

ELSEVIER

Web of Science

1.Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg, Shenzhen, Guangdong, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China
3.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Guangdong, Peoples R China

Wang, Zhenkun,Chen, Yuanyao,Li, Genghui,et al. Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization[J]. SWARM AND EVOLUTIONARY COMPUTATION,2024,91.

Wang, Zhenkun,Chen, Yuanyao,Li, Genghui,Xie, Lindong,&Zhang, Yu.(2024).Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization.SWARM AND EVOLUTIONARY COMPUTATION,91.

Wang, Zhenkun,et al."Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization".SWARM AND EVOLUTIONARY COMPUTATION 91(2024).