An Adaptive Metaheuristic Framework for Changing Environments
<|reference_start|>An Adaptive Metaheuristic Framework for Changing Environments: 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.<|reference_end|>
@article{ahmed2024an,
    title={An Adaptive Metaheuristic Framework for Changing Environments},
    author={Bestoun S. Ahmed},
    journal={arXiv preprint arXiv:2404.12185},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12185},
    primaryClass={cs.AI}
}