# An Adaptive Metaheuristic Framework for Changing Environments

arXiv: https://arxiv.org/abs/2404.12185 · HTML: https://mlsanity.com/paper/2404.12185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Simulation optimization: A review of algorithms and applications](https://mlsanity.com/paper/1706.08591/) (2017)
- [Bio-inspired Optimization: metaheuristic algorithms for optimization](https://mlsanity.com/paper/2003.11637/) (2020)
- [The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for global optimization](https://mlsanity.com/paper/2102.02134/) (2021)

## Built on by (most cited first, 1 of 1)

- [RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework](https://mlsanity.com/paper/2505.13808/) (2025)

## Abstract (arXiv)

The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a dynamic representation of problems, a real-time sensing system, and adaptive techniques to navigate continuously changing optimization environments. Through a simulated dynamic optimization problem, the AMF's capability is demonstrated to detect environmental changes and proactively adjust its search strategy. This framework utilizes a differential evolution algorithm that is improved with an adaptation module that adjusts solutions in response to detected changes. The capability of the AMF to adjust is tested through a series of iterations, demonstrating its resilience and robustness in sustaining solution quality despite the problem's development. The effectiveness of AMF is demonstrated through a series of simulations on a dynamic optimization problem. Robustness and agility characterize the algorithm's performance, as evidenced by the presented fitness evolution and solution path visualizations. The findings show that AMF is a practical solution to dynamic optimization and a major step forward in the creation of algorithms that can handle the unpredictability of real-world problems.