Optimal Design of Functionally Graded Sandwich Porous Beams for Maximum Fundamental Frequency Using Metaheuristics

In this paper, the optimal design of sandwich beams with a functionally graded (FG) porous core and functionally graded faces is freshly addressed by using meta-heuristics. The layer thickness, porosity distribution of the core, and material volume fraction of the face sheets are simultaneously optimized to maximize the fundamental frequency. The work studies the efficiency of some popular meta-heuristics, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), Jaya algorithm, and an adaptive DE algorithm (ANDE), in solving this complicated optimization problem. Moreover, the influence of the beam theories on the optimal design is investigated. Beams with different configurations are examined. It is concluded that the fundamental frequency of the FG sandwich porous beam can be maximized effectively. Among the considered meta-heuristics, ANDE and Jaya appear to be superior to the other algorithms in terms of efficiency and stability. Numerical results further show that the optimal design is affected by the beam theory used, particularly for the thick beam.