Keywords: geometric nonlinearity, TLBO, metaheuristics algorithms, truss optimization, nonlinear finite element analysis, Newton–Raphson.

This study introduces a novel enhancement to the Teaching–Learning-Based Optimization (TLBO) algorithm for structural size optimization of trusses by embedding geometrically nonlinear analysis into the design evaluation process. While traditional TLBO-based truss optimizations typically rely on linear finite element analysis, the proposed framework integrates a Newton–Raphson solver to more accurately capture large-displacement behaviour. This modification allows for a more realistic representation of structural response, especially in flexible or slender truss systems. The optimization aims to minimize the total structural weight by adjusting the cross-sectional areas of the truss members, subject to stress and displacement constraints. Constraint violations are addressed using a quadratic penalty formulation. The classical 10-bar truss problem is employed as a benchmark to validate the method. The results demonstrate that incorporating geometric nonlinearity within the TLBO framework significantly improves the robustness and realism of the optimized designs. This enhanced approach provides a practical alternative for realistic truss design without changing the underlying topology.

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