Review and Computational Study on Practicality of Derivative-Free DIRECT -Type Methods
Pub. online: 26 March 2024
Type: Research Article
Open Access
Open Access
Received
1 October 2023
1 October 2023
Accepted
1 March 2024
1 March 2024
Published
26 March 2024
26 March 2024
Abstract
Derivative-free DIRECT-type global optimization algorithms are increasingly favoured for their simplicity and effectiveness in addressing real-world optimization challenges. This review examines their practical applications through a systematic analysis of scientific journals and computational studies. In particular, significant challenges in reproducibility have been identified with practical problems. To address this, we conducted an experimental study using practical problems from reputable CEC libraries, comparing DIRECT-type techniques against their state-of-the-art counterparts. Therefore, this study sheds light on current gaps, opportunities, and future prospects for advanced research in this domain, laying the foundation for replicating and expanding the research findings presented herein.
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Biographies
Stripinis Linas
L. Stripinis received a PhD degree in informatics from Vilnius University, Lithuania, in 2021. He is currently a researcher at Vilnius University. His research interests include global optimization, optimization software, parallel computing, and machine learning.
Paulavičius Remigijus
R. Paulavičius received a PhD degree in computer science from Vytautas Magnus University, Kaunas, Lithuania, in 2010. He was a postdoctoral researcher at Vilnius University, Vilnius, Lithuania, and a research associate at Imperial College London, London, UK. He is currently a professor and chief researcher at Vilnius University. His research interests include global optimization, optimization software, parallel and quantum computing, and distributed ledger technologies.
