Flower Pollination Algorithm on optimal design of space trusses

Abstract This study assesses the performance of four nature-inspired optimization algorithms—Dynamic Differential Annealed Optimization (DDAO), Flower Pollination Algorithm (FPA), Firefly Algorithm (FF), and Particle Swarm Optimization (PSO) for achieving optimal space truss design. The aim is to minimize the structural weight of three benchmark trusses (10-bar, 25-bar, and 72-bar) while meeting stress and displacement constraints. The key contribution of this work is the first systematic evaluation of FPA in space truss optimization, demonstrating its greater effectiveness in obtaining optimal or near-optimal solutions with faster convergence and higher stability compared to PSO and FF. The results also highlight the limitations of DDAO in handling constrained engineering problems. Findings confirm that FPA and FF are highly effective for structural optimization, offering robust solutions with minimal computational cost. These insights contribute to advancing metaheuristic-based structural design, supporting the adoption of FPA in large-scale optimization problems.