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Multi-Objective High-Fidelity Optimization Using NSGA-III and MO-RPSOLC
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作者 N.Ganesh Uvaraja Ragavendran +2 位作者 Kanak Kalita paras jain Xiao-Zhi Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第11期443-464,共22页
Optimizing the performance of composite structures is a real-world application with significant benefits.In this paper,a high-fidelity finite element method(FEM)is combined with the iterative improvement capability of... Optimizing the performance of composite structures is a real-world application with significant benefits.In this paper,a high-fidelity finite element method(FEM)is combined with the iterative improvement capability of metaheuristic optimization algorithms to obtain optimized composite plates.The FEM module comprises of ninenode isoparametric plate bending element in conjunction with the first-order shear deformation theory(FSDT).A recently proposed memetic version of particle swarm optimization called RPSOLC is modified in the current research to carry out multi-objective Pareto optimization.The performance of the MO-RPSOLC is found to be comparable with the NSGA-III.This work successfully highlights the use of FEM-MO-RPSOLC in obtaining highfidelity Pareto solutions considering simultaneous maximization of the fundamental frequency and frequency separation in laminated composites by optimizing the stacking sequence. 展开更多
关键词 Composites finite element OPTIMIZATION PARETO swarm intelligence
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