Multi-constraint size optimization of planar trusses using a modified firefly algorithm

Abstract

Truss size optimization is computationally a very demanding structural engineering problem due to its non-linear and non-convex nature. Despite being substantially efficient, metaheuristic algorithms like the Firefly Algorithm (FA), converge slowly for big problems. Herein, an Accelerated Firefly Algorithm (AFA) is presented to make the simple FA more efficient and reliable for the optimization of truss size. The AFA enhances performance by utilizing a dynamic randomization parameter and a scaled search strategy to expedite convergence. To evaluate its efficacy, the proposed algorithm was applied to three standard benchmark problems. The outcomes demonstrate that the AFA consistently yields optimal designs that either match or surpass the best-published results found in existing literature. Remarkably, the algorithm exhibited exceptional computational efficiency, converging rapidly while meeting all constraints related to stress and displacement. The findings confirm that the AFA is a promising, effective, and efficient approach for solving complex structural optimization problems and, therefore, well-suited to real-life engineering design problems.