Harnessing dynamic turbulent dynamics in parrot optimization algorithm for complex high-dimensional engineering problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260610" target="_blank" >RIV/61989100:27240/25:10260610 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S004578252500180X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S004578252500180X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cma.2025.117908" target="_blank" >10.1016/j.cma.2025.117908</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Harnessing dynamic turbulent dynamics in parrot optimization algorithm for complex high-dimensional engineering problems

Popis výsledku v původním jazyce

The Parrot Optimization Algorithm (PO) is a nature-inspired metaheuristic algorithm developed based on the social and adaptive behaviors of Pyrrhura molinae parrots. PO demonstrates robust optimization performance by balancing exploration and exploitation, mimicking foraging and cooperative activities. However, as the algorithm progresses through iterations, it faces critical challenges in maintaining search diversity and movement efficiency diminishes, leading to premature convergence and a reduced ability to find optimal solutions in complex search space. To address these limitations, this work introduces the Dynamic Turbulent-based Parrot Optimization Algorithm (DTPO), which represents a significant advancement over the original PO by incorporating three novel strategies: a novel Differential Mutation (DM), Dynamic Opposite Learning (DOL), and Turbulent Operator (TO). The DM Strategy enhances exploration by introducing controlled variations in the population, allowing DTPO to escape local optima. Also, the DOL Strategy dynamically generates opposite solutions to refresh stagnated populations, expanding the search space and maintaining adaptability. Finally, the TO strategy simulates chaotic movements inspired by turbulence, ensuring a thorough local search while preserving population diversity. Together, these strategies improve the algorithm&apos;s ability to explore, exploit, and converge efficiently. Furthermore, the DTPO&apos;s performance was rigorously evaluated on benchmark functions from CEC2017 and CEC2022, comparing it against 23 state-of-the-art algorithms. The results demonstrate DTPO&apos;s superior convergence speed, search efficiency, and optimization accuracy. Additionally, DTPO was tested on seven engineering design problems, achieving significant improvements over the original PO algorithm, with superior performance gains compared to other algorithms in real-world scenarios. Particularly, DTPO outperformed competing algorithms in 37 out of 41 benchmark functions, achieving an overall success rate of 90.24%. Moreover, DTPO obtained the best Friedman ranks across all comparisons, with values ranging from 3.03 to 1.18, demonstrating its superiority over classical, advanced, and recent algorithms. These results validate the proposed enhancements and highlight DTPO&apos;s robustness and effectiveness in solving complex optimization problems. © 2025 Elsevier B.V.

Název v anglickém jazyce

Harnessing dynamic turbulent dynamics in parrot optimization algorithm for complex high-dimensional engineering problems

Popis výsledku anglicky

The Parrot Optimization Algorithm (PO) is a nature-inspired metaheuristic algorithm developed based on the social and adaptive behaviors of Pyrrhura molinae parrots. PO demonstrates robust optimization performance by balancing exploration and exploitation, mimicking foraging and cooperative activities. However, as the algorithm progresses through iterations, it faces critical challenges in maintaining search diversity and movement efficiency diminishes, leading to premature convergence and a reduced ability to find optimal solutions in complex search space. To address these limitations, this work introduces the Dynamic Turbulent-based Parrot Optimization Algorithm (DTPO), which represents a significant advancement over the original PO by incorporating three novel strategies: a novel Differential Mutation (DM), Dynamic Opposite Learning (DOL), and Turbulent Operator (TO). The DM Strategy enhances exploration by introducing controlled variations in the population, allowing DTPO to escape local optima. Also, the DOL Strategy dynamically generates opposite solutions to refresh stagnated populations, expanding the search space and maintaining adaptability. Finally, the TO strategy simulates chaotic movements inspired by turbulence, ensuring a thorough local search while preserving population diversity. Together, these strategies improve the algorithm&apos;s ability to explore, exploit, and converge efficiently. Furthermore, the DTPO&apos;s performance was rigorously evaluated on benchmark functions from CEC2017 and CEC2022, comparing it against 23 state-of-the-art algorithms. The results demonstrate DTPO&apos;s superior convergence speed, search efficiency, and optimization accuracy. Additionally, DTPO was tested on seven engineering design problems, achieving significant improvements over the original PO algorithm, with superior performance gains compared to other algorithms in real-world scenarios. Particularly, DTPO outperformed competing algorithms in 37 out of 41 benchmark functions, achieving an overall success rate of 90.24%. Moreover, DTPO obtained the best Friedman ranks across all comparisons, with values ranging from 3.03 to 1.18, demonstrating its superiority over classical, advanced, and recent algorithms. These results validate the proposed enhancements and highlight DTPO&apos;s robustness and effectiveness in solving complex optimization problems. © 2025 Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Computer Methods in Applied Mechanics and Engineering

ISSN

0045-7825

e-ISSN

—

Svazek periodika

440

Číslo periodika v rámci svazku

MAY

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

54

Strana od-do

1-54

Kód UT WoS článku

001450770700001

EID výsledku v databázi Scopus

2-s2.0-105000418571