Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63563658" target="_blank" >RIV/70883521:28140/23:63563658 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3583133.3595849" target="_blank" >https://dl.acm.org/doi/10.1145/3583133.3595849</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3583133.3595849" target="_blank" >10.1145/3583133.3595849</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking

Popis výsledku v původním jazyce

Despite initial indifference towards boundary control methods (BCM) in the context of metaheuristic algorithm design, benchmarking, and execution, our research demonstrates their critical importance. This study investigates how the choice of a particular BCM can profoundly influence the performance of competitive algorithms. We analyzed the top three algorithms from the 2017 and 2020 IEEE CEC competitions, posing the following question: Could a change in BCM usage alter an algorithm&apos;s overall performance and, consequently, its ranking among competitors? Our findings reveal that paying attention to BCMs can lead to significant improvements. The experiments revealed that BCM selection can significantly impact an algorithm&apos;s performance and, in some instances, its competition rank. However, most authors omitted to mention the implemented BCM, resulting in poor reproducibility and deviating from recommended benchmarking practices for metaheuristic algorithms. The conclusion is that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics.

Název v anglickém jazyce

Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking

Popis výsledku anglicky

Despite initial indifference towards boundary control methods (BCM) in the context of metaheuristic algorithm design, benchmarking, and execution, our research demonstrates their critical importance. This study investigates how the choice of a particular BCM can profoundly influence the performance of competitive algorithms. We analyzed the top three algorithms from the 2017 and 2020 IEEE CEC competitions, posing the following question: Could a change in BCM usage alter an algorithm&apos;s overall performance and, consequently, its ranking among competitors? Our findings reveal that paying attention to BCMs can lead to significant improvements. The experiments revealed that BCM selection can significantly impact an algorithm&apos;s performance and, in some instances, its competition rank. However, most authors omitted to mention the implemented BCM, resulting in poor reproducibility and deviating from recommended benchmarking practices for metaheuristic algorithms. The conclusion is that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics.

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

ISBN

979-840070120-7

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

25-26

Název nakladatele

Association for Computing Machinery, Inc

Místo vydání

New York

Místo konání akce

Lisbon

Datum konání akce

15. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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