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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F25%3APU155576" target="_blank" >RIV/00216305:26210/25:PU155576 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26210/26:0196775

Result on the web

<a href="https://ieeexplore.ieee.org/document/10477219" target="_blank" >https://ieeexplore.ieee.org/document/10477219</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TEVC.2024.3379756" target="_blank" >10.1109/TEVC.2024.3379756</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA24-12474S" target="_blank" >GA24-12474S: Benchmarking derivative-free global optimization methods</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

ISSN

1089-778X

e-ISSN

1941-0026

Volume of the periodical

29

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

187-204

UT code for WoS article

001413496000010

EID of the result in the Scopus database

2-s2.0-85188513523