Parallel Stochastic Global Optimization Using Radial Basis Functions
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Publication:2901075
DOI10.1287/ijoc.1090.0325zbMath1243.90160OpenAlexW2133778423MaRDI QIDQ2901075
Christine A. Shoemaker, Rommel G. Regis
Publication date: 28 July 2012
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/ijoc.1090.0325
Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59)
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MISO: mixed-integer surrogate optimization framework ⋮ Sequential approximate optimization with adaptive parallel infill strategy assisted by inaccurate Pareto front ⋮ Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection ⋮ A surrogate-based cooperative optimization framework for computationally expensive black-box problems ⋮ Uncertainty analysis for computationally expensive models with multiple outputs ⋮ Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for~computationally expensive black-box global optimization problems ⋮ An adaptive framework for costly black-box global optimization based on radial basis function interpolation ⋮ Stochastic optimization with adaptive restart: a framework for integrated local and global learning ⋮ SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems ⋮ GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration
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