.png){kind=icon}
返回
A hyper-intelligent framework for large-scale structural optimisation
.png){kind=icon}
OA
AI
DOI：10.1016/j.istruc.2025.110858.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study introduces a new algorithmic framework, termed the hyper-intelligent (HI) framework, designed to address diverse structural optimisation problems with improved statistical consistency. In this framework, a set of meta-heuristics in the low-level heuristic (LLH) space is applied concurrently by individuals during the optimisation process. Each individual is equipped with a high-level heuristic (HLH) subspace that provides an online, feedback-driven learning mechanism. Two variants are proposed based on the learning strategy: the hyper-intelligent algorithm with a choice function (HIA-CF) and the hyper-intelligent algorithm with a multiarmed bandit (HIA-MAB). The performance of the proposed approach is evaluated using three large-scale structural design examples with static and frequency constraints. The results demonstrate that the HI framework achieves more consistent performance than conventional meta-heuristics. This highlights the advantages of intelligently and adaptively integrating multiple search strategies simultaneously for complex structural design optimisation. The study emphasises the necessity of a paradigm shift in intelligent structural design optimisation.
Keyword:
Hyper-intelligent
Optimal design
Algorithms
Large-scale structures
Optimisation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
1.3W
被引数:
2.7W
机构
引用论文
Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms设计可持续的生物乙醇供应链网络: 机器学习和元启发式算法的结合
PREAI
分享收藏分享收藏On the performance of flax fibres in multi-objective design of laminated composite plates for buckling and cost.png)
STRUCTURES
STRUCTURESIF4.3
PREAI
分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏
查看更多内容