DOI: 10.5281/zenodo.6491863
title: DIRECTGOLib - DIRECT Global Optimization test problems Library

description: With the introduction of <strong>DIRECTGOLib</strong> (which stands as <strong>DIRECT</strong> <strong>G</strong>lobal <strong>O</strong>ptimization test problems <strong>Lib</strong>rary), we present a new and an actively growing online collection of the box- and generally-constrained test and engineering problems for <strong>DIRECT</strong> (<strong>DI</strong>viding <strong>RECT</strong>angles)-type global optimization [5]. <strong>DIRECTGOLib</strong> is a continuation of our previous <strong>DIRECTLib</strong> library [11], which was widely used in different studies (see, e.g., [6, 7, 8, 9, 10]). However, <strong>DIRECTLib</strong> was designed as a static library and did not offer the global optimization community comfortable opportunities to contribute to its growth. Therefore, a new <strong>DIRECTGOLib</strong> is designed as an open-source GitHub repository to which other researchers can easily contribute. Moreover, all the problems are described using <code>MATLAB</code> programming language and syntax, seeking maximum usability with our recently introduced open-source tool: DIRECTGO: A new DIRECT-type toolbox for derivative-free Global Optimization. <strong>Problems</strong> There already exist various collections of global optimization test problems. The uniqueness of this collection is that it mainly concentrates on problems commonly used to test various DIRECT-type algorithms with at least one reliable source of experimental results. While the problems are gathered from the various sources but below we highlight a few that form an essential part of the current version: Global Optimization Test Problems (Hedar list) [1] Virtual Library of Simulation Experiments: Test Functions and Datasets [2] CEC2006 benchmark set [3] Global bound and linear constrained problems [4] Parameter estimation in the general non-linear regression model [12] Engineering design examples [13] <strong>Classification</strong> Based on the type of constraints, continuous global optimization test problems from <strong>DIRECTGOLib</strong> are classified into three main categories and the number of test problems within each category of the current version is specified in brackets: Box-constrained (47 problems in total) Linearly-constrained (35 problems in total) Generally-constrained (39 problems in total) We also separate problems coming from practical applications: Engineering problems (11 problems in total). <strong>Contribution to DIRECTGOLib</strong> We welcome contributions and corrections to this resource either way: <strong>By email</strong> - send us new problems, corrections, or suggestions by email: linas.stripinis@mif.vu.lt or remigijus.paulavicius@mif.vu.lt. <strong>GitHub way</strong> - fork the GitHub repository, add new problems or correct existing ones, then create a pull request, and we gratefully incorporate your contribution! <strong>References</strong> A. 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(2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. doi:10.1007/978-1-4614-9093-7. Stripinis, L., Paulavičius, R. & Žilinskas, J. Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT. Optim Lett 12, 1699–1712 (2018). https://doi.org/10.1007/s11590-017-1228-4. Stripinis, L., Paulavičius, R. & Žilinskas, J. Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization. Struct Multidisc Optim 59, 2155–2175 (2019). https://doi.org/10.1007/s00158-018-2181-2. Stripinis, L., Paulavičius, R. A new DIRECT-GLh algorithm for global optimization with hidden constraints. Optim Lett 15, 1865–1884 (2021). https://doi.org/10.1007/s11590-021-01726-z. Stripinis, L., Žilinskas, J., Casado, L. G., & Paulavičius, R. (2021). On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. 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