RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework

TL;DR

This paper introduces a self-adaptive, AI-driven metaheuristic framework that dynamically switches algorithms based on real-time feedback, significantly improving optimization performance in complex, dynamic environments.

Contribution

The novel Polymorphic Metaheuristic Framework (PMF) enables real-time algorithm switching using performance feedback, enhancing adaptability and efficiency over traditional fixed-structure metaheuristics.

Findings

PMF outperforms traditional metaheuristics on benchmark functions.

It improves convergence speed and solution quality.

The framework effectively balances exploration and exploitation.

Abstract

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.