Grey Wolf Optimization with Inequality Constraints for Non-Convex Optimization with Application to Engineering Science

Document Type : Research Paper

Authors

1 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

2 Department of Surveying Engineering, Faculty of Engineering, Golestan University, Aliabad Katoul, Iran

Abstract

Here, an improved meta-heuristic method is proposed to solve one of the most challenging nonlinear problems known as non-convex problems. In this type of problem, we are faced with more than one minimum, all of which do not lead to the correct solution. This method is based on equipping the grey wolf meta-heuristic algorithm with inequality constraints. Although various forms of the grey wolf algorithm have been proposed in the last few years, none of them included inequality constraints, especially in the form presented here. The main advantage of the proposed algorithm in engineering applications is solving non-convex optimization problems where analytical methods may get stuck in a local minimum, while the aforementioned algorithm is located in a global minimum. With two mathematical test functions and one geodetic example, the efficiency of the proposed approach is evaluated.

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References

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Journal of Modeling in Engineering
Volume 23, Issue 83
December 2025
Pages 145-155

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History

  • Receive Date: 15 July 2024
  • Revise Date: 14 March 2025
  • Accept Date: 06 April 2025

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