用AI生成多样且高效的优化算法，解决传统方法单一化问题
QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design
- 结合大模型与进化计算，通过代码嵌入保持算法多样性
- 在多个基准和工业场景中超越现有方法，提升性能与效率
- 适合需要多解方案的复杂优化问题求解者使用
大型语言模型（LLMs）与进化计算的结合已成为组合优化中自动化启发式设计的强大范式。然而，现有方法存在模式坍缩问题，导致种群趋同、语义多样性不足，无法充分探索算法空间。我们提出质量-多样性进化框架QDEvo，将质量-多样性优化与基于LLM的启发式搜索相结合，利用预训练代码嵌入维护无限规模的语义多样化算法档案，并引入分层自省机制引导进化过程。在标准基准和真实工业应用中的大量实验表明，QDEvo在超体积（Hypervolume）和逆世代距离（Inverted Generational Distance）指标上显著优于当前最优方法。该框架可发现兼具高性能、低计算开销与高语义多样性的启发式算法，为复杂优化问题提供丰富的解决方案组合。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in combinatorial optimization. However, existing approaches suffer from mode collapse, converging to homogeneous populations that lack semantic diversity and fail to explore the full algorithmic space. We propose Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-driven heuristic search, maintaining an unbounded archive of semantically diverse algorithms using pre-trained code embeddings and incorporating hierarchical self-reflection to guide the evolutionary process. Extensive experiments across standard benchmarks and real-world industrial applications demonstrate that QDEvo significantly outperforms state-of-the-art methods in both Hypervolume and Inverted Generational Distance metrics. Our framework enables the discovery of heuristics that are simultaneously high-performing, computationally efficient, and semantically diverse, providing practitioners with a rich portfolio of solutions for complex optimization problems.
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