用AI动态切换优化算法，自动提升求解效率与质量。
RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework
- 基于实时性能反馈，自动在多种算法间切换。
- 在高维多峰问题中收敛更快，避免陷入局部最优。
- 适合需要智能自适应的工程与决策系统应用。
元启发式算法广泛用于求解复杂优化问题，但其性能常受限于固定结构和繁琐调参。本文提出的多态元启发式框架（PMF）通过引入基于实时性能反馈的自适应算法切换机制，克服了这一局限。PMF利用多态元启发式代理（PMA）和选择代理（PMSA），根据关键性能指标动态选择并切换算法，实现持续自适应。该方法显著提升了收敛速度、适应性与解的质量，在高维、动态及多峰环境中优于传统元启发式算法。在基准函数上的实验表明，PMF有效缓解了算法停滞问题，并在各类问题景观中平衡了探索与利用策略。通过融合人工智能驱动的决策与自校正机制，PMF为可扩展、智能且自主的优化框架提供了新路径，有望应用于工程、物流及复杂决策系统。
原文摘要 · Abstract (English)
Metaheuristic algorithms are widely used for solving complex optimization problems, yet their effectiveness is often constrained by fixed structures and the need for extensive tuning. The Polymorphic Metaheuristic Framework (PMF) addresses this limitation by introducing a self-adaptive metaheuristic switching mechanism driven by real-time performance feedback and dynamic algorithmic selection. PMF leverages the Polymorphic Metaheuristic Agent (PMA) and the Polymorphic Metaheuristic Selection Agent (PMSA) to dynamically select and transition between metaheuristic algorithms based on key performance indicators, ensuring continuous adaptation. This approach enhances convergence speed, adaptability, and solution quality, outperforming traditional metaheuristics in high-dimensional, dynamic, and multimodal environments. Experimental results on benchmark functions demonstrate that PMF significantly improves optimization efficiency by mitigating stagnation and balancing exploration-exploitation strategies across various problem landscapes. By integrating AI-driven decision-making and self-correcting mechanisms, PMF paves the way for scalable, intelligent, and autonomous optimization frameworks, with promising applications in engineering, logistics, and complex decision-making systems.
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