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Codes for Surrogate Model Based Optimization

SOCEMO Surrogate Optimization of Computationally Expensive Multi-Objective Problems

This MATLAB implementation uses surrogate model optimization techniques to solve computationally expensive multi-objective black-box optimization problems with box constraints. All optimization parameters have to be continuous.

SOCEMO (MATLAB only) code click to download the zip archive

SOCEMO Matlab manual

Journal article: J. Mueller. SOCEMO: Surrogate Optimization of Computationally Expensive Multi-Objective Problems, INFORMS Journal on Computing, 2017, 29(4):581-596

MISO Mixed Integer Surrogate Optimization framework

A MATLAB implementation of a surrogate model algorithm for computationally expensive mixed-integer black-box optimization problems with box constraints. Allows to choose from different radial basis function types, sampling strategies, and initial experimental design options.

MISO (MATLAB only) code click to download the zip archive

MISO Matlab manual

Journal article: J. Mueller. MISO: Mixed-Integer Surrogate Optimization framework, Optimization and Engineering, 17(1): 177-203, 2016

MATSuMoTo MATLAB Surrogate Model Toolbox

Surrogate model toolbox for box-constrained global optimization problems (continuous, pure integer, mixed-integer). Contains various surrogate model mixtures, initial experimental design strategies, and sampling strategies

MATSuMoTo (MATLAB only) code

MATSuMoTo Matlab manual

J. Mueller. MATSuMoTo: The MATLAB Surrogate Model Toolbox for Computationally Expensive Black-Box Global Optimization Problems, arXiv:1404.4261, April, 2014

Stochastic RBF codes - implementation based on this paper

Surrogate model optimization algorithm applicable for computationally expensive, black-box global optimization problems. MATLAB version requires MATLAB 2010b or newer. For doing several evaluations in each iteration, MATLAB Parallel Computing Toolbox is required. Python version requires Python 2.7.

Stochastic RBF Python code

Stochastic RBF Python manual

Stochastic RBF MATLAB code

Stochastic RBF MATLAB manual

DYCORS codes - implementation based on this paper

Surrogate model optimization algorithm applicable for computationally expensive, black-box global optimization problems with large dimensions (>30). MATLAB version requires MATLAB 2010b or newer. For doing several evaluations in each iteration, MATLAB Parallel Computing Toolbox is required. Python version requires Python 2.7.

DYCORS Python code

DYCORS Python manual

DYCORS MATLAB code

DYCORS MATLAB manual