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A hyper-intelligent framework for large-scale structural optimisation
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DOI：10.1016/j.istruc.2025.110858.png)
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Abstract
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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.
Keywords:
Hyper-intelligent
Optimal design
Algorithms
Large-scale structures
Optimisation
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