Smart Computing and Emerging Technologies
Ant Colony Optimization: Principles, Variants, and Application Domains – A Survey
Authors: Deepak Kumar Pathak, Aishwarya Mishra, Koastubh Ahlawat and Lavika Goel
Publishing Date: 29-10-2025
ISBN: 978-81-975670-0-1
Abstract
Ant Colony Optimization (ACO) is a population-based metaheuristic inspired by the foraging behaviour of real ant colonies. Since its inception, ACO has been successfully applied to a wide range of combinatorial and continuous optimization problems due to its distributed, adaptive, and positive feedback-based search mechanism. This survey presents a comprehensive overview of the foundational principles, variants, and key enhancements of the ACO algorithm. We critically analyse its performance in classical application domains such as the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Job Shop Scheduling, and explore its growing role in modern contexts like wireless sensor networks, bioinformatics, image processing, and machine learning. Furthermore, the paper discusses hybridization techniques, parameter tuning strategies, and the integration of ACO with deep learning and evolutionary algorithms. Challenges such as premature convergence, scalability, and real-time applicability are addressed, and future research directions are proposed. This survey aims to serve as a valuable resource for researchers and practitioners interested in leveraging ACO for solving complex real-world optimization problems.
Keywords
Ant Colony Optimization, Metaheuristic Algorithms, Combinatorial Optimization, Swarm Intelligence,Hybrid Algorithms, Geoscience inspired algorithms
Cite as
Deepak Kumar Pathak, Aishwarya Mishra, Koastubh Ahlawat and Lavika Goel, "Ant Colony Optimization: Principles, Variants, and Application Domains – A Survey", In: Himanshu Mittal (eds), Smart Computing and Emerging Technologies, SCRS, India, 2025, pp. 43-58. https://doi.org/10.56155/978-81-975670-0-1-5