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.

翻译：现代优化问题环境的快速变化要求算法具备实时自适应能力。本文提出一种专为动态环境设计的自适应元启发式框架（AMF）。该框架能够智能适应问题参数的变化，通过结合问题的动态表征、实时感知系统以及自适应技术，在持续变化的优化环境中进行导航。通过模拟动态优化问题，验证了AMF检测环境变化并主动调整搜索策略的能力。该框架采用经改进的差分进化算法，其内置自适应模块可根据检测到的变化动态调整解决方案。通过系列迭代测试，证实AMF在问题演进过程中仍能保持解决方案质量的韧性与鲁棒性。基于动态优化问题的多组仿真实验展示了AMF的有效性，通过适应度演化曲线与解路径可视化图表可知，算法性能兼具鲁棒性与敏捷性。研究结果表明，AMF是解决动态优化问题的实用方案，标志着在处理现实问题不可预测性的算法创建方面取得了重大进展。