Detail projektu

Benchmarking derivative-free global optimization methods

Období řešení: 1.1.2024 — 31.12.2026

Zdroje financování

Grantová agentura České republiky - Standardní projekty

- plně financující

O projektu

The project aims the research of benchmarking techniques for derivative-free optimization methods. The two main directions in the development of these optimization methods are mathematical programming (e.g. the DIRECT method) and evolutionary algorithms (e.g. the differential evolution algorithm). The term benchmarking refers to a set of procedures for comparing such methods. Appropriately chosen benchmarking techniques can reveal the structural bias of some methods, or create guidelines for choosing suitable methods for a given optimization problem. Some of the current issues in this field are the strong emphasis on artificially created benchmark sets, the structural problems of some sets and algorithms, and the small intersection between benchmarking techniques and the comparison of methods from the two main development directions mentioned above.

Klíčová slova
global optimization;derivative-free methods;benchmarking

Označení

24-12474S

Originální jazyk

angličtina

Řešitelé

Kůdela Jakub, doc. Ing., Ph.D. - hlavní řešitel

Útvary

Ústav automatizace a informatiky
- odpovědné pracoviště (28.3.2023 - nezadáno)
Ústav automatizace a informatiky
- příjemce (28.3.2023 - nezadáno)

Výsledky

KŮDELA, J.; DOBROVSKÝ, L.; SHEHADEH, M.; HŮLKA, T.; MATOUŠEK, R. Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals. In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. ISBN: 979-8-3503-0836-5.
Detail

KŮDELA, J.; JUŘÍČEK, M.; PARÁK, R.; TZANETOS, A.; MATOUŠEK, R. Benchmarking Derivative-Free Global Optimization Methods on Variable Dimension Robotics Problems. In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. ISBN: 979-8-3503-0836-5.
Detail

KŮDELA, J.; DOBROVSKÝ, L. Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems. In 18th International Conference on Parallel Problem Solving from Nature. Springer Science and Business Media Deutschland GmbH, 2024. p. 303-321. ISBN: 978-3-031-70068-2.
Detail

TZANETOS, A.; KŮDELA, J. Working on the Structural Components of Evolutionary Approaches. In Proceedings of the 16th International Joint Conference on Computational Intelligence. Science and Technology Publications, Lda, 2024. p. 375-382. ISBN: 978-989-758-721-4.
Detail

STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Hot Off the Press: Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets. In 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion. Association for Computing Machinery, Inc, 2024. p. 57-58. ISBN: 979-8-4007-0495-6.
Detail

STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2025, vol. 29, no. 1, p. 187-204. ISSN: 1089-778X.
Detail