An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization

DOI10.1016/J.INS.2020.01.048zbMath1457.90150OpenAlexW3004157659MaRDI QIDQ2660961

Sebastian Schmitt, Markus Olhofer, Xilu Wang, Yaochu Jin

Publication date: 31 March 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.01.048

zbMATH Keywords

Bayesian optimizationreference vectorsacquisition functionsurrogate-assisted evolutionary algorithmexpensive multi-objective optimization

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29)

Related Items (2)

An efficient constrained global optimization algorithm with a clustering-assisted multiobjective infill criterion using Gaussian process regression for expensive problems ⋮ An adaptive batch Bayesian optimization approach for expensive multi-objective problems

Uses Software

• DACE
• MOEA/D
• SPEA2
• EGO
• RM-MEDA
• ParEGO
• Spearmint
• PlatEMO

Cites Work

• Survey of multi-objective optimization methods for engineering
• Efficient global optimization of expensive black-box functions
• Efficient global optimization algorithm assisted by multiple surrogate techniques
• Advanced Lectures on Machine Learning
• Evolutionary Multi-Criterion Optimization

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