Open Access

ARTICLE

Optimization of Truss Structures Using Nature-Inspired Algorithms with Frequency and Stress Constraints

Sanjog Chhetri Sapkota^1,2, Liborio Cavaleri³, Ajaya Khatri⁴, Siddhi Pandey⁵, Satish Paudel⁶, Panagiotis G. Asteris^7,*

1 Department of Civil Engineering, Sharda University, Knowledge Park III, Greater Noida, 201310, India
2 Nepal Research and Collaboration Center, Bhakti Thapa Sadak, Baneshwor, Kathmandu, 44600, Nepal
3 Department of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Palermo, 90128, Italy
4 Department of Civil Engineering, Pashchimanchal Campus, Institute of Engineering, Tribhuvan University, Pokhara, 33700, Nepal
5 Department of Structural Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, 12120, Thailand
6 Department of Civil and Environmental Engineering, University of Nevada, 1664 N Virginia St., Reno, NV 89557, USA
7 Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Marousi, Athens, 15122, Greece

* Corresponding Authors: Panagiotis G. Asteris. Email: ,

(This article belongs to the Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)

Computer Modeling in Engineering & Sciences 2026, 146(1), 14 https://doi.org/10.32604/cmes.2025.069691

Received 28 June 2025; Accepted 28 November 2025; Issue published 29 January 2026

Abstract

Optimization is the key to obtaining efficient utilization of resources in structural design. Due to the complex nature of truss systems, this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints. Two new algorithms, the Red Kite Optimization Algorithm (ROA) and Secretary Bird Optimization Algorithm (SBOA), are utilized on five benchmark trusses with 10, 18, 37, 72, and 200-bar trusses. Both algorithms are evaluated against benchmarks in the literature. The results indicate that SBOA always reaches a lighter optimal. Designs with reducing structural weight ranging from 0.02% to 0.15% compared to ROA, and up to 6%–8% as compared to conventional algorithms. In addition, SBOA can achieve 15%–20% faster convergence speed and 10%–18% reduction in computational time with a smaller standard deviation over independent runs, which demonstrates its robustness and reliability. It is indicated that the adaptive exploration mechanism of SBOA, especially its Levy flight–based search strategy, can obviously improve optimization performance for low- and high-dimensional trusses. The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA, a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.

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Keywords

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