This study investigates the effectiveness of using Ant Colony Optimization (ACO) algorithms for random walks sampling in the Facebook network of the Massachusetts Institute of Technology (MIT). Random walks sampling is a crucial technique for network analysis, enabling an understanding of the network’s state irrespective of the starting node. By implementing an ACO algorithm, this research demonstrates an efficient method of sampling that ensures all nodes are sampled with uniform probability. The ACO algorithm leverages heuristic methods to significantly reduce the warm-up time required to obtain a sample. Experimental results confirm that the ACO implementation achieves the expected outcomes, demonstrating its efficiency in random sampling by reducing the number of jumps needed. This reduction in warm-up time, along with the uniform sampling capability, positions ACO as a promising alternative to traditional random walk algorithms for network analysis. The findings underscore the potential of bio-inspired algorithms in enhancing network sampling methodologies, offering both theoretical and practical implications for future research in this domain.