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

Lei, Yanhua; Huang, Mengzhen

doi:https://doi.org/10.5194/ms-16-673-2025

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

Metrics will be available soon.

Latest update: 03 Nov 2025

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.