Many-Objective Grey Wolf Optimizer (MaOGWO) for solving real-world problems
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Many-Objective Grey Wolf Optimizer (MaOGWO) for solving real-world problems
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20300 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Results in Engineering
ISSN
2590-1230
e-ISSN
2590-1230
Volume of the periodical
28
Issue of the periodical within the volume
DEC 2025
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
23
Pages from-to
nestránkováno
UT code for WoS article
001584178700001
EID of the result in the Scopus database
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