Maximizing efficiency: A comparative study of SOMA variants and constraint handling methods for time delay system optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570998" target="_blank" >RIV/70883521:28140/23:63570998 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3583133.3596417" target="_blank" >https://dl.acm.org/doi/10.1145/3583133.3596417</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3583133.3596417" target="_blank" >10.1145/3583133.3596417</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Maximizing efficiency: A comparative study of SOMA variants and constraint handling methods for time delay system optimization

Popis výsledku v původním jazyce

This paper presents an experimental study that compares four adaptive variants of the self-organizing migrating algorithm (SOMA). Each variant uses three different constraint handling methods for the optimization of a time delay system model. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delayed systems to develop more effective and efficient control strategies and precise model identifications. The study includes a detailed description of the selected variants of the SOMA and the adaptive mechanisms used. A complex workflow of experiments is described, and the results and discussion are presented. The experimental results highlight the effectiveness of the SOMA variants with specific constraint handling methods for time delay system optimization. Overall, this study contributes to the understanding of the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of the SOMA variants and can help guide the selection of appropriate constraint handling methods and the adaptive mechanisms of metaheuristics. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Název v anglickém jazyce

Maximizing efficiency: A comparative study of SOMA variants and constraint handling methods for time delay system optimization

Popis výsledku anglicky

This paper presents an experimental study that compares four adaptive variants of the self-organizing migrating algorithm (SOMA). Each variant uses three different constraint handling methods for the optimization of a time delay system model. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delayed systems to develop more effective and efficient control strategies and precise model identifications. The study includes a detailed description of the selected variants of the SOMA and the adaptive mechanisms used. A complex workflow of experiments is described, and the results and discussion are presented. The experimental results highlight the effectiveness of the SOMA variants with specific constraint handling methods for time delay system optimization. Overall, this study contributes to the understanding of the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of the SOMA variants and can help guide the selection of appropriate constraint handling methods and the adaptive mechanisms of metaheuristics. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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/GF21-45465L" target="_blank" >GF21-45465L: Metaheuristicky založená parametrická optimalizace modelů a řídicích systémů s dopravním zpožděním</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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

9

Strana od-do

1821-1829

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

001117972600295