The effect of smart nanoparticles on the computational optimization of beams using cloud-based framework for computer simulation

The study evaluates how smart nanoparticles affect beam structural performance while using computational resources hosted on remote servers. An enhanced advanced adaptive harmony search algorithm (AAHS) serves to boost optimization efficiency levels. The algorithm makes two sequential parameter adjustment stages which start by adapting harmony memory through variable bandwidth methods and proceed with adaptive step-size implementations. The research investigates the best design parameters for ZnO nanoparticle reinforced nanocomposite sinusoidal beams under different axial force and foundation property conditions and applied voltage levels. Results show that the proposed AAHS method outperforms alternative optimization methods according to comparative research. Under 50 GPa buckling force and 100 V applied voltage the optimal beam should have L/h ratio of 4.425 together with 118 GPa foundation spring constant, 29 Pa shearing constant and 0.055 ZnO nanoparticle volume fraction. The study demonstrates that all three factors namely applied voltage, buckling force and foundation stiffness critically affect optimization of beams.