Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary Optimization

Genghui Li1; Zhenkun Wang2; Maoguo Gong3

2022

10.1109/TSMC.2022.3219080

IEEE Transactions on Systems, Man, and Cybernetics: Systems   Impact Factor & Quartile

2168-2232

2168-2232

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for solving expensive optimization problems. However, it still faces challenges when dealing with complex and high-dimensional problems. To fill this gap, a new algorithm (called SAMFEO) that combines surrogate-assisted and model-free evolutionary optimization is proposed in this article. SAMFEO consists of a local surrogate-assisted multioperator evolutionary optimization (LSA-MoEO) and a model-free single-operator evolutionary optimization (MF-SoEO). Specifically, LSA-MoEO adopts multiple evolutionary operators to generate a set of offspring and prescreens the best one as the final offspring by using a lightweight local surrogate model trained by some newest evaluated solutions. MF-SoEO follows the traditional evolutionary optimization paradigm and is triggered based on the optimization utility of the LSA-MoEO. It plays a crucial role in preventing the population from getting stagnation. Experimental results show that SAMFEO has significant advantages over several state-of-the-art SAEAs on some complex benchmark problems and one real-world problem.

Expensive Optimization Model-free Evolutionary Optimization Multioperator Surrogate-assisted Evolutionary Optimization

SCI

English

First

National Natural Science Foundation of China["62036006","62106096","62206120"] ; Shenzhen Technology Plan[JCYJ20220530113013031]

Automation & Control Systems ; Computer Science

Automation & Control Systems ; Computer Science, Cybernetics

WOS:000886834700001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

IEEE

1.School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China
2.School of System Design and Intelligent Manufacturing and the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
3.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an, China

Genghui Li,Zhenkun Wang,Maoguo Gong. Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary Optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2022,PP(99):1-12.

Genghui Li,Zhenkun Wang,&Maoguo Gong.(2022).Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary Optimization.IEEE Transactions on Systems, Man, and Cybernetics: Systems,PP(99),1-12.

Genghui Li,et al."Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary Optimization".IEEE Transactions on Systems, Man, and Cybernetics: Systems PP.99(2022):1-12.