Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251901" target="_blank" >RIV/61989100:27240/22:10251901 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0378475422002774?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0378475422002774?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.matcom.2022.06.007" target="_blank" >10.1016/j.matcom.2022.06.007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

Popis výsledku v původním jazyce

Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA&apos;s U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Název v anglickém jazyce

Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

Popis výsledku anglicky

Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA&apos;s U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Mathematics and computers in simulation

ISSN

0378-4754

e-ISSN

1872-7166

Svazek periodika

202

Číslo periodika v rámci svazku

prosinec 2022

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

31

Strana od-do

343-373

Kód UT WoS článku

000822807100002

EID výsledku v databázi Scopus

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