<p>The recently proposed Horned Lizard Optimization Algorithm (HLOA) has demonstrated considerable optimization capabilities and the desirable trait of being parameter-free, positioning it as a promising swarm intelligence paradigm. However, its performance is hampered by an imbalance between exploration and exploitation, which tends to result in sluggish convergence rate and early entrapment in local optimal solutions, especially when tackling complex problems with high dimension. To overcome these limitations, this paper introduces ROBEHLOA, an enhanced algorithm that synergistically integrates four strategies: Opposition-Based Learning (OBL) and Biogeography-based Learning Strategy (BLS) to accelerate convergence and augment global search capabilities, complemented by Random Following (RF) and a Hierarchical Structure (HS) to improve performance in complex optimization landscapes. To validate its efficacy in the discrete domain, ROBEHLOA is adapted into a binary variant, designated bROBEHLOA, specifically for the task of Feature Selection (FS). The performance of ROBEHLOA and its binary variant is substantiated through exhaustive experimentation. On the CEC 2017 benchmark functions, ROBEHLOA demonstrates marked superiority over the original HLOA and 19 other well-regarded Metaheuristic algorithms (MAs). In its application to FS, the binary version bROBEHLOA is rigorously evaluated on 20 public datasets, where it consistently achieves a superior balance of higher classification accuracy and more compact feature subsets when compared to nine prominent binary MAs. Collectively, these findings validate ROBEHLOA as a robust and high-performance algorithm for global optimization, establishing its binary variant as an advanced competitive method for FS.</p>