RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework
Faramarz Safi Esfahani, Ghassan Beydoun, Morteza Saberi, Brad McCusker, Biswajeet Pradhan·May 20, 2025
Summary
The Polymorphic Metaheuristic Framework (PMF) dynamically selects and transitions between algorithms for optimization, using real-time performance feedback. It features the Polymorphic Metaheuristic Agent (PMA) and Polymorphic Metaheuristic Selection Agent (PMSA) to improve convergence, adaptability, and solution quality, surpassing traditional methods in complex, high-dimensional problems. AI-driven decision-making and self-correcting mechanisms make PMF scalable and suitable for engineering, logistics, and decision-making systems.
Introduction
Background
Overview of metaheuristic algorithms
Challenges in traditional optimization methods
Objective
To present a novel framework that dynamically selects and transitions between algorithms for optimization
Highlighting the role of AI-driven decision-making and self-correcting mechanisms
The Polymorphic Metaheuristic Agent (PMA)
Functionality
Overview of the PMA's role in the PMF
How the PMA adapts to different optimization problems
Key Features
Real-time performance feedback integration
Dynamic algorithm selection based on problem characteristics
The Polymorphic Metaheuristic Selection Agent (PMSA)
Functionality
Role of the PMSA in the PMF
How the PMSA facilitates the transition between algorithms
Key Features
Real-time performance analysis
Adaptive algorithm selection for enhanced optimization
Improving Convergence, Adaptability, and Solution Quality
Enhanced Convergence
Strategies for faster convergence
Case studies demonstrating improved convergence rates
Enhanced Adaptability
Dynamic adjustment to problem complexity
Examples of adapting to varying optimization landscapes
Improved Solution Quality
Techniques for higher-quality solutions
Comparative analysis with traditional methods
Scalability and Application Areas
Scalability
How the PMF scales with problem size and complexity
Discussion on computational efficiency and resource management
Application Areas
Engineering optimization
Logistics and supply chain management
Decision-making systems in various industries
AI-Driven Decision-Making and Self-Correcting Mechanisms
AI Integration
Role of AI in algorithm selection and performance prediction
How AI enhances the PMF's adaptability and decision-making
Self-Correcting Mechanisms
Explanation of the PMF's ability to self-correct and improve over time
Case studies showcasing self-correction in real-world applications
Conclusion
Summary of the PMF's contributions
Future directions and potential advancements
Implications for research and practical applications
Basic info
papers
neural and evolutionary computing
artificial intelligence
Advanced features