This paper presents the constrained Hybrid Metaheuristic (cHM) algorithm as a general framework for continuous optimisation. Unlike many existing metaheuristics that are tailored to specific function classes or problem domains, cHM is designed to operate across a broad spectrum of objective functions, including those with unknown, heterogeneous, or complex properties such as non-convexity, non-separability, and varying smoothness. We provide a formal description of the algorithm, highlighting its modular structure and two-phase operation, which facilitates dynamic adaptation to the problem's characteristics. A key feature of cHM is its ability to harness synergy between both candidate solutions and component metaheuristic strategies. This property allows the algorithm to apply the most appropriate search behaviour at each stage of the optimisation process, thereby improving convergence and robustness. Our extensive experimental evaluation on 28 benchmark functions demonstrates that cHM consistently matches or outperforms traditional metaheuristics in terms of solution quality and convergence speed. In addition, a practical application of the algorithm is demonstrated for a feature selection problem in the context of data classification. The results underscore its potential as a versatile and effective black-box optimiser suitable for both theoretical research and practical applications.

翻译：本文提出了约束混合元启发式（cHM）算法，作为连续优化的一种通用框架。与许多针对特定函数类别或问题领域量身定制的现有元启发式算法不同，cHM旨在处理广泛的目标函数，包括那些具有未知、异构或复杂特性（如非凸性、不可分离性和变平滑性）的函数。我们提供了该算法的形式化描述，强调了其模块化结构和两阶段操作，这有助于动态适应问题特征。cHM的一个关键特性是其能够利用候选解与组件元启发式策略之间的协同作用。这一特性使算法能够在优化过程的每个阶段应用最合适的搜索行为，从而提高收敛速度和鲁棒性。我们在28个基准函数上的广泛实验评估表明，cHM在解质量和收敛速度方面始终与传统的元启发式算法持平或优于它们。此外，本文还展示了该算法在数据分类特征选择问题中的实际应用。结果凸显了其作为适用于理论研究和实际应用的通用且有效的黑盒优化器的潜力。