Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

DOI10.1007/s10898-010-9620-yzbMath1230.90155OpenAlexW2078363790MaRDI QIDQ652658

Robert Piché, Juliane Müller

Publication date: 15 December 2011

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-010-9620-y

zbMATH Keywords

global optimizationDempster-Shafer theoryresponse surfacemixture surrogate models

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (14)

MISO: mixed-integer surrogate optimization framework ⋮ Surrogate‐based methods for black‐box optimization ⋮ An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems ⋮ Robust optimization of subsurface flow using polynomial chaos and response surface surrogates ⋮ A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization ⋮ Quantifying uncertainty with ensembles of surrogates for blackbox optimization ⋮ GOSAC: global optimization with surrogate approximation of constraints ⋮ Calibration of GARCH models using concurrent accelerated random search ⋮ Order-based error for managing ensembles of surrogates in mesh adaptive direct search ⋮ SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems ⋮ Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints ⋮ Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for~computationally expensive black-box global optimization problems ⋮ Locally weighted regression models for surrogate-assisted design optimization ⋮ SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications

Uses Software

• DACE
• EGO

Cites Work

• Constrained global optimization of expensive black box functions using radial basis functions
• Improved strategies for radial basis function methods for global optimization
• An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
• EGO shape optimization of Horn-loaded loudspeakers
• Coupled aerostructural design optimization using the Kriging model and integrated multiobjective optimization algorithm
• On the Dempster-Shafer framework and new combination rules
• Multivariate adaptive regression splines
• Efficient global optimization of expensive black-box functions
• A taxonomy of global optimization methods based on response surfaces
• Global optimization of costly nonconvex functions using radial basis functions
• Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory
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