2025/09/10 by Yisong Zhang, Zhang, Yisong, Ran Cheng +5 · 4 citations
Computer Science · Materials Science · #Advanced Multi-Objective Optimization Algorithms #Key (lock) #Machine Learning in Materials Science #Metaheuristic Optimization Algorithms Research #Perspective (graphical) #Point (geometry) #Process (computing) #Selection (genetic algorithm) #Systematic review #cs.AI #cs.NE
paper · pdf · doi:10.48550/arxiv.2509.08269
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks. This survey primarily focuses on evolutionary optimization, i.e., optimization based on evolutionary computation. For brevity, we use the term optimization throughout to denote this scope. However, existing surveys typically examine isolated roles of LLMs and do not provide a unified view that connects optimization modeling with optimization solving. To address this gap, we systematically review recent developments through a workflow-oriented framework. First, we organize the literature into two primary stages: LLMs for optimization modeling and LLMs for optimization solving (in this survey, the terms optimization modeling and optimization solving are used as concise forms of optimization problem modeling and optimization problem solving, respectively). Second, we divide the solving stage into three paradigms according to the role of the LLM: stand-alone optimizers, low-level components embedded in optimization algorithms, and high-level managers for algorithm selection and generation. Third, we analyze representative methods, identify their technical limitations, and clarify their relationships with traditional optimization approaches. We further substantiate this taxonomy through benchmark systematization, baseline comparisons, and practitioner-oriented guidance, and we review interdisciplinary applications across the natural sciences, engineering, and machine learning. Based on the resulting analysis, we identify research directions toward dynamic, self-evolving, and agentic optimization ecosystems. An up-to-date collection of related literature is maintained at https://github.com/ishmael233/LLM4OPT.