Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-673-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-16-673-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
03 Nov 2025
Research article |  | 03 Nov 2025

An adaptive improved gray wolf optimization algorithm with dynamic constraint handling for mechanism-constrained optimization problems

Yanhua Lei and Mengzhen Huang

Viewed

Total article views: 1,207 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
948 171 88 1,207 49 69
  • HTML: 948
  • PDF: 171
  • XML: 88
  • Total: 1,207
  • BibTeX: 49
  • EndNote: 69
Views and downloads (calculated since 03 Nov 2025)
Cumulative views and downloads (calculated since 03 Nov 2025)

Cited

Latest update: 18 Sep 2026
Download
Short summary
To enhance the convergence speed, accuracy, and high-dimensional constrained optimization problem-solving capability of the traditional gray wolf optimization algorithm, an improved gray wolf optimization algorithm is proposed by integrating an optimal individual memory strategy, a position update strategy with differential variance, and a discrete crossover factor. Experimental results show that the improved algorithm outperforms other comparative ones.
Read more
Share