{"ID":2881566,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11871","arxiv_id":"2508.11871","title":"LLM4CMO: Large Language Model-aided Algorithm Design for Constrained Multiobjective Optimization","abstract":"Constrained multi-objective optimization problems (CMOPs) frequently arise in real-world applications where multiple conflicting objectives must be optimized under complex constraints. Existing dual-population two-stage algorithms have shown promise by leveraging infeasible solutions to improve solution quality. However, designing high-performing constrained multi-objective evolutionary algorithms (CMOEAs) remains a challenging task due to the intricacy of algorithmic components. Meanwhile, large language models (LLMs) offer new opportunities for assisting with algorithm design; however, their effective integration into such tasks remains underexplored. To address this gap, we propose LLM4CMO, a novel CMOEA based on a dual-population, two-stage framework. In Stage 1, the algorithm identifies both the constrained Pareto front (CPF) and the unconstrained Pareto front (UPF). In Stage 2, it performs targeted optimization using a combination of hybrid operators (HOps), an epsilon-based constraint-handling method, and a classification-based UPF-CPF relationship strategy, along with a dynamic resource allocation (DRA) mechanism. To reduce design complexity, the core modules, including HOps, epsilon decay function, and DRA, are decoupled and designed through prompt template engineering and LLM-human interaction. Experimental results on six benchmark test suites and ten real-world CMOPs demonstrate that LLM4CMO outperforms eleven state-of-the-art baseline algorithms. Ablation studies further validate the effectiveness of the LLM-aided modular design. These findings offer preliminary evidence that LLMs can serve as efficient co-designers in the development of complex evolutionary optimization algorithms. The code associated with this article is available at https://anonymous.4open.science/r/LLM4CMO971.","short_abstract":"Constrained multi-objective optimization problems (CMOPs) frequently arise in real-world applications where multiple conflicting objectives must be optimized under complex constraints. Existing dual-population two-stage algorithms have shown promise by leveraging infeasible solutions to improve solution quality. Howeve...","url_abs":"https://arxiv.org/abs/2508.11871","url_pdf":"https://arxiv.org/pdf/2508.11871v2","authors":"[\"Zhen-Song Chen\",\"Hong-Wei Ding\",\"Xian-Jia Wang\",\"Witold Pedrycz\"]","published":"2025-08-16T02:00:57Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","project_urls":"[\"https://anonymous.4open.science/r/LLM4CMO971\"]","has_code":false}