Journal
INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS
Volume 19, Issue 3, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219876221500651
Keywords
Laminated functionally graded carbon nanotube-reinforced composite quadrilateral plates; multi-objective optimization; deep feedforward neural network; nondominated sorting genetic algorithm II
Funding
- Vietnam National Foundation for Science and Technology Development (NAFOSTED) [107.02-2019.330]
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This paper proposes a novel and efficient approach that integrates deep feedforward neural network and nondominated sorting genetic algorithm II to solve multi-objective optimization problems. This method saves computational cost while providing high-accuracy optimal solutions.
This paper proposes a novel and efficient DNN-NSGAII approach which is an integration of the deep feedforward neural network (DNN) and the nondominated sorting genetic algorithm II (NSGAII) to solve multi-objective optimization (MOO) problems of laminated functionally graded carbon nanotube-reinforced composite (FG-CNTRC) quadrilateral plates. The core idea of the proposed approach is to use the DNN as an analyzer to evaluate the objective and constraint functions instead of using the time-consuming finite element analysis methods (FEAMs) during the optimization process, while the NSGAII is employed to find a set of Pareto-optimal solutions of the MOO problems. Accordingly, the proposed DNN-NSGAII remarkably saves the computational cost, but still provides a high accurate optimal solution. The precision, efficiency, and capability of the proposed method are demonstrated through two different MOO problems of the FG-CNTRC quadrilateral plates. Obtained results of the DNN-NSGAII are compared with those of other methods to investigate the reliability and efficiency of proposed method. Moreover, the effects of various boundary conditions and carbon nanotube (CNT) distributions on the Pareto-optimal solutions of MOO problems are also examined.
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