Many-Objective Grey Wolf Optimizer (MaOGWO) for solving real-world problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259964" target="_blank" >RIV/61989100:27230/25:10259964 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001584178700001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001584178700001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.106941" target="_blank" >10.1016/j.rineng.2025.106941</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Many-Objective Grey Wolf Optimizer (MaOGWO) for solving real-world problems

Popis výsledku v původním jazyce

The Grey Wolf Optimizer (GWO) is an effective optimization algorithm that primarily focuses on single objective optimization and is characterized by its simplicity and fast convergence. However, adapting GWO for manyobjective optimization requires addressing two critical challenges: keeping the solutions in sync while also promoting variety. In this study, a new Many-Objective Grey Wolf Optimizer (MaOGWO) is proposed which includes reference point strategies, niche preservation and information feedback to enhance convergence and diversity. A comprehensive comparative analysis was conducted to evaluate MaOGWO against four leading many-objective optimization algorithms: Some of them are Non-dominated Sorting Genetic Algorithm III (NSGAIII), Many-Objective Particle Swarm Optimizer (MaOPSO), Many-Objective Teaching Learning-Based Optimizer (MaOTLBO) and Many-Objective Gradient-Based Optimizer (MaOGBO). We evaluated the performance of these algorithms on the DTLZ1-DTLZ7 problem sets with 5, 10 and 15 objectives and five real-world many-objective optimization problems, namely RWMaOP1-RWMaOP5. The quality assessment metrics used in this research were generational distance (GD), inverted generational distance (IGD), spacing (SP), spread (SD), hypervolume (HV) and runtime (RT). The experimental outcomes revealed that the MaOGWO dominated the other algorithms in terms of convergence to the Pareto-optimal front and solution diversity for both synthetic and practical problems with lower computational costs.

Název v anglickém jazyce

Many-Objective Grey Wolf Optimizer (MaOGWO) for solving real-world problems

Popis výsledku anglicky

The Grey Wolf Optimizer (GWO) is an effective optimization algorithm that primarily focuses on single objective optimization and is characterized by its simplicity and fast convergence. However, adapting GWO for manyobjective optimization requires addressing two critical challenges: keeping the solutions in sync while also promoting variety. In this study, a new Many-Objective Grey Wolf Optimizer (MaOGWO) is proposed which includes reference point strategies, niche preservation and information feedback to enhance convergence and diversity. A comprehensive comparative analysis was conducted to evaluate MaOGWO against four leading many-objective optimization algorithms: Some of them are Non-dominated Sorting Genetic Algorithm III (NSGAIII), Many-Objective Particle Swarm Optimizer (MaOPSO), Many-Objective Teaching Learning-Based Optimizer (MaOTLBO) and Many-Objective Gradient-Based Optimizer (MaOGBO). We evaluated the performance of these algorithms on the DTLZ1-DTLZ7 problem sets with 5, 10 and 15 objectives and five real-world many-objective optimization problems, namely RWMaOP1-RWMaOP5. The quality assessment metrics used in this research were generational distance (GD), inverted generational distance (IGD), spacing (SP), spread (SD), hypervolume (HV) and runtime (RT). The experimental outcomes revealed that the MaOGWO dominated the other algorithms in terms of convergence to the Pareto-optimal front and solution diversity for both synthetic and practical problems with lower computational costs.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

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

28

Číslo periodika v rámci svazku

DEC 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

23

Strana od-do

nestránkováno

Kód UT WoS článku

001584178700001

EID výsledku v databázi Scopus

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