MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems

TL;DR

MATSuMoTo is a MATLAB toolbox designed for efficient global optimization of expensive black-box functions with mixed variable types, utilizing surrogate models and parallel computing to reduce the number of costly function evaluations.

Contribution

It introduces a flexible MATLAB toolbox that combines various surrogate models, sampling strategies, and parallel evaluation capabilities for black-box optimization.

Findings

Effective in reducing the number of expensive function evaluations.

Supports mixed-integer and continuous variables.

Leverages MATLAB's Parallel Computing Toolbox for parallel evaluations.

Abstract

MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally expensive, black-box, global optimization problems that may have continuous, mixed-integer, or pure integer variables. Due to the black-box nature of the objective function, derivatives are not available. Hence, surrogate models are used as computationally cheap approximations of the expensive objective function in order to guide the search for improved solutions. Due to the computational expense of doing a single function evaluation, the goal is to find optimal solutions within very few expensive evaluations. The multimodality of the expensive black-box function requires an algorithm that is able to search locally as well as globally. MATSuMoTo is able to address these challenges. MATSuMoTo offers various choices for surrogate models and surrogate model mixtures, initial experimental design strategies, and sampling strategies. MATSuMoTo is able to do several function evaluations in parallel by exploiting MATLAB's Parallel Computing Toolbox.