Ant Colony Clustering and Optimization: A Detailed Comparative Review of Applications in Diverse Domains

Ant Colony Optimization (ACO) and Ant Colony Clustering (ACC), inspired by the decentralized foraging behavior of ants, have emerged as highly effective metaheuristics for addressing complex, large-scale optimization and unsupervised learning challenges in modern computing. This article reviews contemporary research from 2016-2025, synthesizing the application and enhancement of these algorithms across three critical domains. Key findings demonstrate ACO’s capability to achieve significant resource efficiency in Network Optimization, including a reported 9.646% reduction in node power usage in Wireless Sensor Networks (WSN) and highly competitive latency minimization in Edge Computing. Furthermore, ACC is validated as a superior solution for Dynamic Data Stream Mining, utilizing memory-efficient, single-scan methods for real-time clustering. In specialized applications, such as the Traveling Salesman Problem (TSP) and Bio-Computational Analysis, hybrid ACO variants successfully overcome limitations like premature convergence and statistical bias by integrating community network analysis and novel heuristic criteria. The evidence confirms that customized ACO and ACC algorithms provide scalable, self-organizing solutions that outperform traditional methods, driving forward resource-aware and intelligent distributed systems.