# RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework

arXiv: https://arxiv.org/abs/2505.13808 · HTML: https://mlsanity.com/paper/2505.13808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 8 of 8)

- [LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics](https://mlsanity.com/paper/2405.20132/) (2024)
- [Leveraging Large Language Model to Generate a Novel Metaheuristic Algorithm with CRISPE Framework](https://mlsanity.com/paper/2403.16417/) (2024)
- [Reinforcement learning based adaptive metaheuristics](https://mlsanity.com/paper/2206.12233/) (2022)
- [Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach](https://mlsanity.com/paper/2405.18272/) (2024)
- [AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures](https://mlsanity.com/paper/2204.00998/) (2022)
- [Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments](https://mlsanity.com/paper/2201.00523/) (2022)
- [Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization](https://mlsanity.com/paper/2402.02033/) (2024)
- [An Adaptive Metaheuristic Framework for Changing Environments](https://mlsanity.com/paper/2404.12185/) (2024)

## Abstract (arXiv)

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.