Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Optimization of structure using hybrid Harris hawks and Sine-cosine algorithm

Document Type : Original Article

Authors
1 Associate professor, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 PhD Candidate, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Abstract
In this study, an efficient hybrid algorithm is proposed based on the Harris Hawks Optimization (HHO) algorithm and the sine cosine algorithm (SCA) for the optimal design of truss structures. Harris Hawks Optimization algorithm consists of two main phases: exploration and exploitation. Transitions between these phases are determined by the magnitude of the prey's escape energy. A key limitation of the Harris Hawks Optimization is its tendency to get stuck in local optima and its relatively slow convergence speed. The Sine Cosine Algorithm (SCA) finds the solution to an optimization problem by leveraging the behaviour of sine and cosine functions. The aim of this study is to establish an effective synergy between the exploration and exploitation stages in the combined algorithm utilizing the Sine Cosine Algorithm. In the size optimization of a truss, the section area of the members is considered a decision variable. The objective function aims to minimize the weight of the truss while ensuring that member stresses and node displacements remain within specified limits. The combined Hawk-Sine-Cosine algorithm (HSCA) was implemented using the MATLAB software. To quantitatively assess the algorithm's performance, three planar trusses (10 bars, 18 bars, and 200 bars) and three space trusses (25 bars, 72 bars, and 942 bars) with multiple loading conditions and design constraints have been considered. The results indicate that the Harris-Sine Cosine algorithm is highly effective and efficient for optimizing large-scale truss structures. The Harris-Sine Cosine algorithm demonstrates a superior ability to avoid local optima when compared to HHO and most population-based algorithms.
Keywords

Subjects


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  • Receive Date 17 April 2024
  • Revise Date 03 September 2024
  • Accept Date 02 October 2024