<p>Exploration and exploitation are fundamental components of metaheuristic algorithms (henceforth MAs), essential for optimizing performance and achieving superior results. Exploration involves visiting diverse regions within the search space, while exploitation focuses on refining solutions near previously visited areas. Balancing these components is critical, necessitating accurate measurement and control during the optimization process. This paper presents a comprehensive review and empirical evaluation of strategies for quantifying and analyzing exploration-exploitation dynamics in MAs. We investigate eight well-established algorithms, using 12 benchmark functions from the CEC 2022 suite to assess the validity of various measurement techniques. Multiple statistical and visual tools, including 3D trajectory maps, heat maps, and attraction basins, are employed to examine these dynamics. We also analyze Pearson and Spearman correlation coefficients and exploration ratios for each algorithm across the selected indicators. The results reveal that the Attraction Basin and Diversity-based methods most accurately capture the true exploration-exploitation behaviour. In parallel, the TLBO and OOBO algorithms consistently demonstrate superior performance across various test functions and dimensions, owing to their well-regulated balance. These findings are reinforced by a detailed statistical analysis, including Friedman tests. To the best of our knowledge, this study is the first to offer such a comprehensive investigation, contributing to the field of optimization by guiding future research and aiding in the development of more robust and efficient MAs.</p>