Vol.129, No.2, 2021, pp.443-464, doi:10.32604/cmes.2021.014960
Multi-Objective High-Fidelity Optimization Using NSGA-III and MO-RPSOLC
  • N. Ganesh1, Uvaraja Ragavendran2, Kanak Kalita3,*, Paras Jain4, Xiao-Zhi Gao5
1 Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
2 Department of Electronics and Telecommunication Engineering, MPSTME SVKM’S Narsee Monjee Institute of Management Studies, Shirpur, 425405, India
3 Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600062, India
4 School of Computing Science and Engineering, VIT Bhopal University, Sehore, 466114, India
5 School of Computing, University of Eastern Finland, Kuopio, 70211, Finland
* Corresponding Author:Kanak Kalita. Email:
Received 12 November 2020; Accepted 03 June 2021; Issue published 08 October 2021
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.
Composites; finite element; optimization; Pareto; swarm intelligence
Cite This Article
Ganesh, N., Ragavendran, U., Kalita, K., Jain, P., Gao, X. (2021). Multi-Objective High-Fidelity Optimization Using NSGA-III and MO-RPSOLC. CMES-Computer Modeling in Engineering & Sciences, 129(2), 443–464.
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