Detail publikace

Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets

STRIPINIS, L. KŮDELA, J. PAULAVIČIUS, R.

Originální název

Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.

Klíčová slova

Derivative-free global optimization; deterministic algorithms; evolutionary computation (EC) algorithms; nature-inspired meta-heuristics; numerical benchmarking

Autoři

STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R.

Vydáno

1. 2. 2025

Nakladatel

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Místo

PISCATAWAY

ISSN

1089-778X

Periodikum

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

Ročník

29

Číslo

1

Stát

Spojené státy americké

Strany od

187

Strany do

204

Strany počet

18

URL

https://ieeexplore.ieee.org/document/10477219

BibTex

@article{BUT196775,
  author="Linas {Stripinis} and Jakub {Kůdela} and Remigijus {Paulavičius}",
  title="Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets",
  journal="IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION",
  year="2025",
  volume="29",
  number="1",
  pages="187--204",
  doi="10.1109/TEVC.2024.3379756",
  issn="1089-778X",
  url="https://ieeexplore.ieee.org/document/10477219"
}