Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F24%3APU155646" target="_blank" >RIV/00216305:26210/24:PU155646 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10611951" target="_blank" >https://ieeexplore.ieee.org/document/10611951</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC60901.2024.10611951" target="_blank" >10.1109/CEC60901.2024.10611951</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals

Popis výsledku v původním jazyce

Surrogate-assisted evolutionary algorithms (SAEAs) are currently among the most widely researched techniques for their capability to solve expensive real-world optimization problems. The development of these techniques and their bench-marking with other methods still relies almost exclusively on artificially created problems. In this paper, we use a real-world problem of optimizing the parameters of a hospital resource planning tool to compare the performance of nine state-of-the-art single-objective SAEAs. We find that there are significant differences between the performance of the compared methods on the selected instances, making the problems suitable for benchmarking SAEAs.

Název v anglickém jazyce

Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals

Popis výsledku anglicky

Surrogate-assisted evolutionary algorithms (SAEAs) are currently among the most widely researched techniques for their capability to solve expensive real-world optimization problems. The development of these techniques and their bench-marking with other methods still relies almost exclusively on artificially created problems. In this paper, we use a real-world problem of optimizing the parameters of a hospital resource planning tool to compare the performance of nine state-of-the-art single-objective SAEAs. We find that there are significant differences between the performance of the compared methods on the selected instances, making the problems suitable for benchmarking SAEAs.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA24-12474S" target="_blank" >GA24-12474S: Benchmarking globálních optimalizačních metod</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE Congress on Evolutionary Computation (CEC)

ISBN

979-8-3503-0836-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

„“-„“

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Yokohama

Datum konání akce

30. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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