TL;DR
A hierarchical intelligent optimization framework is proposed and Large Language Models (LLMs) are introduced as strategy optimizers, thereby proposing the LLM-HSO algorithm, which leverages the contextual understanding and code generation capabilities of LLMs to perform evaluation and strategy reconstruction during the evolutionary process.
Experimental results demonstrate that LLMDE achieves competitive performance on the benchmark suite while continuously generating high-quality solutions for complex constrained optimization tasks, and successfully demonstrate the viability of embedding LLMs within metaheuristics, paving a promising path toward the desi...
Solving high-dimensional structural model updating problems requires an algorithm capable of navigating complex, non-convex landscapes with correlated parameters. Existing hybrid evolutionary algorithms typically rely on static architectures or fixed switching rules, resulting in disjointed search phases. To address th...
Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorithms. Many successful algorithm-design agents adopt pure in-context evolutionary frameworks, but they may quickly plateau in domains that require specialized knowledge. P...
Learning-based hyper-heuristics for combinatorial optimization select from among low-level operators or tune the parameters of a single metaheuristic. However, online selection among complete population-based metaheuristics that share one evolving population and its joint configuration through a common action space hav...
Dynamic resource configuration in regional industrial clusters is pivotal for unlocking their collective efficiency and ensuring resilience in cyber-physical production systems. However, static optimization methods suffer from a fundamental mismatch against volatile industrial rhythms, lacking the situational awareness...
Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are predominantly evaluated on small, self-contained textual problems and often commit to a solver-integrated paradigm, limiting their ability to handle the scale and struc...
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