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DOI: 10.14569/IJACSA.2022.01310106
PDF

The Multi-Objective Design of Laminated Structure with Non-Dominated Sorting Genetic Algorithm

Author 1: Huiyao Zhang
Author 2: Yuxiao Wang
Author 3: Fangmeng Zeng

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

  • Abstract and Keywords
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Abstract: Non-dominated sorting genetic algorithm has shown excellent advantages in solving complicated optimization problems with discrete variables in a variety of domains. In this paper, we implement a multi-objective genetic algorithm to guide the design of the laminated structure with two objectives: minimizing the mass and maximizing the strength of a specified structure simultaneously, classical lamination theory and failure theory are adopted to compute the strength of a laminate. The simulation results have shown that a non-dominated genetic algorithm has great advantages in the design of laminated composite material. Experiment results also suggest that optimal run times are from 16 to 32 for the design of glass-epoxy laminate with non-dominated sorting genetic algorithm. We also observed that two stages involve the optimization process in which the number of individuals in the first frontier first increases, and then decreases. These simulation results are helpful to decide the proper run times of genetic algorithms for glass-epoxy design and reduce computation costs.

Keywords: Non-dominated sorting genetic algorithm; optimization; failure theory; laminated composite material; classical lamination theory

Huiyao Zhang, Yuxiao Wang and Fangmeng Zeng, “The Multi-Objective Design of Laminated Structure with Non-Dominated Sorting Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01310106

@article{Zhang2022,
title = {The Multi-Objective Design of Laminated Structure with Non-Dominated Sorting Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01310106},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01310106},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Huiyao Zhang and Yuxiao Wang and Fangmeng Zeng}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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