Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection

DOI10.1007/s10898-015-0270-yzbMath1339.90293OpenAlexW1999433604WikidataQ59479938 ScholiaQ59479938MaRDI QIDQ905751

Christine A. Shoemaker, Taimoor Akhtar

Publication date: 28 January 2016

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

Full work available at URL: https://doi.org/10.1007/s10898-015-0270-y

zbMATH Keywords

global optimizationradial basis functionsparallelfunction approximationmetamodelmulti objective optimizationevolutionary optimizationexpensive optimization

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (11)

Surrogate‐based methods for black‐box optimization ⋮ Integrating \(\varepsilon \)-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems ⋮ A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser ⋮ MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation ⋮ A stochastic adaptive radial basis function algorithm for costly black-box optimization ⋮ Constrained multiobjective optimization of expensive black-box functions using a heuristic branch-and-bound approach ⋮ SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems ⋮ An adaptive framework for costly black-box global optimization based on radial basis function interpolation ⋮ On the use of polynomial models in multiobjective directional direct search ⋮ Performance indicators in multiobjective optimization ⋮ SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

Uses Software

• MOEA/D
• AQUARS
• MLMSRBF
• EGO
• SO-I
• ParEGO
• SMS-EMOA
• ORBIT

Cites Work

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