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<p>The enormous design space for impact-resistant metamaterials is means that traditional trial-and-error methods and numerical simulations can be extremely costly. Based on dynamic theory and efficient optimization strategies, this study describes a method for optimizing the global parameters that affect the performance of metamaterials. A dynamic model of elastic metamaterials is constructed, and the Newmark method is applied to determine the system’s response. Latin hypercube sampling is used to generate 35 sets of sample points. Based on the experimental results, a high-accuracy response surface model is established, with the minimum displacement amplitude and minimum system mass as its output objectives. The non-dominated sorting genetic algorithm II is used to optimize the established approximate model and obtain the Pareto frontier solution set. The results indicate that the established response surface model can effectively describe the relationship between influencing factors and targets. The metamaterial incorporating the optimized structural parameters is found to achieve a substantial reduction in impact amplitude and decrease the overall weight of the structure.</p>

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Multi-objective optimization for global parameter design of impact-resistant metamaterials

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Tong Li,
  • Leilei Zhao,
  • Chenyue Zhang,
  • Xiaodong Sun,
  • Xianghai Chai

摘要

The enormous design space for impact-resistant metamaterials is means that traditional trial-and-error methods and numerical simulations can be extremely costly. Based on dynamic theory and efficient optimization strategies, this study describes a method for optimizing the global parameters that affect the performance of metamaterials. A dynamic model of elastic metamaterials is constructed, and the Newmark method is applied to determine the system’s response. Latin hypercube sampling is used to generate 35 sets of sample points. Based on the experimental results, a high-accuracy response surface model is established, with the minimum displacement amplitude and minimum system mass as its output objectives. The non-dominated sorting genetic algorithm II is used to optimize the established approximate model and obtain the Pareto frontier solution set. The results indicate that the established response surface model can effectively describe the relationship between influencing factors and targets. The metamaterial incorporating the optimized structural parameters is found to achieve a substantial reduction in impact amplitude and decrease the overall weight of the structure.