Coupled particle swarm optimization method with genetic algorithm for the static-dynamic performance of the magneto-electro-elastic nanosystem

As a first attempt, Fourier series expansion (FSE), particle swarm optimization (PSO), and genetic algorithm (GA) methods are coupled for analysis of the static-dynamic performance and propagated waves in the magneto-electro-elastic (MEE) nanoplate. The FSE method is presented for solving the motion equations of the MEE nanoplate. For increasing the performance of genetic algorithms for solving the problem, the particle swarm optimization technique is added as an operator of the GA. Accuracy, convergence, and applicability of the proposed mixed approach are shown in the results section. Also, we prove that for obtaining the convergence results of the PSO and GA, we should consider more than 16 iterations. Finally, it is shown that if designers consider the presented algorithm in their model, the results of phase velocity of the nanosystem will be increased by 27%. A useful suggestion is that there is a region the same as a trapezium in which there are no effects from magnetic and electric potential of the MEE face sheet on the phase velocity of the smart nanoplate, and the region will be bigger by increasing the wavenumber.

Automated summary

The study coupled Fourier series expansion (FSE), particle swarm optimization (PSO), and genetic algorithm (GA) methods to analyze the static-dynamic performance and wave propagation in magneto-electro-elastic (MEE) nanoplates. By using the PSO technique as an operator of GA, the performance of genetic algorithms was enhanced. Results demonstrated the need for over 16 iterations to achieve convergence of the PSO and GA, and showed a 27% increase in phase velocity of the nanosystem when incorporating the proposed algorithm.