Structural Engineering and Mechanics

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Weighted sum multi-objective optimization of skew composite laminates

  • Kalita, Kanak (Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering, Science and Technology) ;
  • Ragavendran, Uvaraja (Department of Electronics and Telecommunication Engineering, MPSTME SVKM'S Narsee Monjee Institute of Management Studies) ;
  • Ramachandran, Manickam (Department of Mechanical Engineering, MPSTME SVKM'S Narsee Monjee Institute of Management Studies) ;
  • Bhoi, Akash Kumar (Department of Electrical & Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University)
  • Received : 2018.09.01
  • Accepted : 2018.11.27
  • Published : 2019.01.10
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Abstract

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

Keywords

References

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