An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50022472" target="_blank" >RIV/62690094:18470/25:50022472 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.rineng.2025.104215" target="_blank" >https://doi.org/10.1016/j.rineng.2025.104215</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.104215" target="_blank" >10.1016/j.rineng.2025.104215</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization

Popis výsledku v původním jazyce

This work presents the Whale migrating Algorithm (WMA), an innovative bio-inspired metaheuristic optimization method based on the collaborative migrating behavior of humpback whales. In contrast to conventional methods, WMA integrates leader-follower dynamics with adaptive migratory tactics to balance exploration and exploitation, improving its capacity to evade local optima and converge effectively. The performance of the proposed algorithm was meticulously assessed using the CEC-2005, CEC-2014, and CEC-2017 optimization problems and some restricted engineering problems, exhibiting enhanced accuracy, robustness, and convergence velocity relative to leading optimization techniques, such as PSO, WOA, and GWO. These findings confirm WMA is an effective instrument for addressing intricate optimization challenges across several domains. The source code of the WMA is publicly available at https://www.optim-app.com/projects/wma.

Název v anglickém jazyce

An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization

Popis výsledku anglicky

This work presents the Whale migrating Algorithm (WMA), an innovative bio-inspired metaheuristic optimization method based on the collaborative migrating behavior of humpback whales. In contrast to conventional methods, WMA integrates leader-follower dynamics with adaptive migratory tactics to balance exploration and exploitation, improving its capacity to evade local optima and converge effectively. The performance of the proposed algorithm was meticulously assessed using the CEC-2005, CEC-2014, and CEC-2017 optimization problems and some restricted engineering problems, exhibiting enhanced accuracy, robustness, and convergence velocity relative to leading optimization techniques, such as PSO, WOA, and GWO. These findings confirm WMA is an effective instrument for addressing intricate optimization challenges across several domains. The source code of the WMA is publicly available at https://www.optim-app.com/projects/wma.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Results in engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

25

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

34

Strana od-do

"Article Number: 104215"

Kód UT WoS článku

001425758800001

EID výsledku v databázi Scopus

2-s2.0-85217206001