<p>To investigate the question of decision-making individuals in distributed multi-agent systems, this study introduces the concept of front boundary agents based on observations of various collective movements in biological swarms and the notion of boundary sets in topology. A formal definition of the model of front boundary agents is provided. Considering the inherent uncertainty of real-world boundaries, we designed two algorithms to identify the set of front boundary agents: the Upper Convex Hull (<i>UCH</i>) algorithm and the Fast Front Boundary Detection (<i>FFBD</i>) algorithm. To compare the performance of these two algorithms, we conducted simulation experiments in regular and random clusters and randomly selected eight experimental clusters to design a questionnaire survey. The survey responses were subsequently compiled and ranked. Finally, three evaluation metrics-goodness of fit, directed Hausdorff distance, and mean derivation-were employed to assess the consistency between the algorithmic outputs and the survey results. The evaluation demonstrates that the <i>FFBD</i> algorithm satisfies the “majority rule” principle and significantly outperforms the <i>UCH</i> algorithm in terms of both accuracy and stability.</p>