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Coupled particle swarm optimization method with genetic algorithm for the static–dynamic performance of the magneto-electro-elastic nanosystem
Engineering with Computers ( IF 8.7 ) Pub Date : 2021-04-15 , DOI: 10.1007/s00366-021-01391-x
Jian Jiao , Seyed-mohsen Ghoreishi , Zohre Moradi , Khaled Oslub

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.



中文翻译:

遗传算法结合粒子群优化算法求解磁电弹性纳米系统的静动力性能

作为首次尝试,将傅里叶级数展开(FSE),粒子群优化(PSO)和遗传算法(GA)方法耦合用于分析磁电弹性(MEE)纳米板中的静态-动态性能和传播波。提出了FSE方法来求解MEE纳米板的运动方程。为了提高解决问题的遗传算法的性能,添加了粒子群优化技术作为遗传算法的运算符。结果部分显示了所提出的混合方法的准确性,收敛性和适用性。同样,我们证明了为了获得PSO和GA的收敛结果,我们应该考虑16个以上的迭代。最后表明,如果设计人员在其模型中考虑所提出的算法,纳米系统的相速度的结果将增加27%。一个有用的建议是,存在与梯形相同的区域,其中MEE面板的磁和电势对智能纳米板的相速度没有影响,并且该区域将通过增加波数而变大。

更新日期:2021-04-15
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