Optimal Design of Steel Planar Trusses Using Ant Lion Algorithm

Abstract

This paper elaborates on optimized design of steel structures directed towards the sustainability of materials. The case in point is steel trusses that are extensively used structural components. Though copious research is available on use of conventional optimization methods, nature-inspired optimization algorithms have received scarce attention particularly in optimal design of planar trusses. In this paper, the development of Ant Lion algorithm for the optimal design models for steel trusses is elaborated. A comprehensive comparison with the optimized sectional weights obtained by other nature inspired optimization algorithms implemented in earlier research by the author. They include elitism based genetic algorithm (EBGA), ant colony optimization (ACO), artificial honeybee optimization (AHBO), and Particle swarm optimization (PSO) algorithm. Four steel trusses with different articulations have been considered for this purpose. It is found that the optimal weights obtained by Ant Lion algorithm are almost on par with those obtained by PSO. The other three algorithms vary marginally. However, the convergence to overall weight of trusses is different for different algorithms. ALO took 100-200 iterations for the convergence. In fact, the convergence to optimized weights are faster in case of ALO and PSO in relation to other algorithms.