International Journal of Nonlinear Analysis and Applications

Improved Coot optimization algorithm with Levy flight for shape and size optimization of Truss structures

Document Type : Research Paper

Authors

Faculty of Technical and Engineering, Department of Civil engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract

The convergence issues and getting trapped in local optimal points are two of the major concerns in the field of optimization. For this purpose, improving the standard algorithms to reach better performance in facing complex optimization problems is considered as one of the main challenges in the field of optimization. In this paper, the performance improvement of metaheuristic algorithms is considered while the applicability of the improved and standard algorithms is evaluated through the weight optimization problem of truss structures. For this purpose, the recently proposed Coot optimization algorithm is utilized as the main algorithm which is inspired by different movement types of Coot birds in the water in order to reach food supplies. Regarding the fact that the standard Coot algorithm utilizes random movement in the main search loop, a new improving methodology is utilized in this paper by replacing these random movements with Levy flight as a stochastic procedure with step length defined by levy distribution. The performance of the standard and improved Coot optimization algorithms is investigated in dealing with the problem of optimizing the shape and size of truss structures. Based on the best and statistical results, it is concluded that the improved Coot algorithm is capable of providing better results that the standard Coot algorithm while the capability of the improving methods in increasing the overall performance of the standard algorithm is demonstrated.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 16, Issue 2
February 2025
Pages 325-342
  • Receive Date: 14 January 2024
  • Revise Date: 02 March 2024
  • Accept Date: 08 March 2024