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Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: the advantage of more elementary-level analyses

  • Kanak Kalita
  • Tanmoy MukhopadhyayEmail author
  • Partha Dey
  • Salil Haldar
Original Article

Abstract

High-fidelity multi-scale design optimization of many real-life applications in structural engineering still remains largely intractable due to the computationally intensive nature of numerical solvers like finite element method. Thus, in this paper, an alternate route of metamodel-based design optimization methodology is proposed in multi-scale framework based on a symbolic regression implemented using genetic programming (GP) coupled with d-optimal design. This approach drastically cuts the computational costs by replacing the finite element module with appropriately constructed robust and efficient metamodels. Resulting models are compact, have good interpretability and assume a free-form expression capable of capturing the non-linearly, complexity and vastness of the design space. Two robust nature-inspired optimization algorithms, viz. multi-objective genetic algorithm and multi-objective particle swarm optimization, are used to generate Pareto optimal solutions for several test problems with varying complexity. TOPSIS, a multi-criteria decision-making approach, is then applied to choose the best alternative among the Pareto optimal sets. Finally, the applicability of GP in efficiently tackling multi-scale optimization problems of composites is investigated, where a real-life scenario is explored by varying fractions of pertinent engineering materials to bring about property changes in the final composite structure across two different scales. The study reveals that a microscale optimization leads to better optimized solutions, demonstrating the advantage of carrying out a multi-scale optimization without any additional computational burden.

Keywords

Multi-scale optimization Machine learning-based optimization Genetic programming Symbolic regression d-Optimal design Robust composite structures 

Notes

Acknowledgements

KK acknowledges the financial support from MHRD, India, through the award of Ph.D. Scholarship during the period of this research work.

Supplementary material

521_2019_4280_MOESM1_ESM.docx (22 kb)
Supplementary material 1 (DOCX 21 kb)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Kanak Kalita
    • 1
  • Tanmoy Mukhopadhyay
    • 2
    Email author
  • Partha Dey
    • 3
  • Salil Haldar
    • 1
  1. 1.Department of Aerospace Engineering and Applied MechanicsIndian Institute of Engineering, Science and TechnologyHowrahIndia
  2. 2.Department of Aerospace EngineeringIndian Institute of Technology KanpurKanpurIndia
  3. 3.Department of Mechanical EngineeringAcademy of TechnologyHooghlyIndia

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