Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a datasets, thereby enhancing the classifier's performance. The Grey Wolf Optimizer (GWO) is a well-known meta-heuristic algorithm rooted in swarm intelligence. It is widely used in various optimization problems due to its fast convergence and minimal parameter requirements. However, in the context of solving high-dimensional classification problems, GWO's global search capability is limited, and it is susceptible to getting trapped in local optima. To address this, we introduce an Adaptive Mechanism-based Grey Wolf Optimizer (AMGWO) for FS in high-dimensional classification. This approach encompasses a novel nonlinear parameter control strategy to balance exploration and exploitation effectively, thereby preventing the algorithm from converging prematurely. Additionally, an adaptive fitness distance balancing mechanism is proposed to prevent premature convergence and enhance search efficiency by selecting high-potential solutions. Lastly, an adaptive neighborhood mutation mechanism is designed to adjust mutation intensity adaptively during the search process, allowing AMGWO to more effectively find the global optimum. To validate the proposed AMGWO method, we assess its performance on 15 high-dimensional datasets and compare it with the original GWO and five of its variants in terms of classification accuracy, feature subset size, and execution speed, thus confirming the superiority of AMGWO. [ABSTRACT FROM AUTHOR]

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