Gurpreet Singh, Neeraj Kumar
This article explores the application of Ant Colony Optimization (ACO) algorithms within the realm of swarm intelligence, known for addressing various optimization challenges including continuous and mixed discrete-continuous variable problems. The objective of the study is to derive the evolutionary dynamics of ACO, which mimics the behavior of real ant colonies in finding the shortest paths between food sources and their nests. Utilizing artificial ants and pheromone trails to enhance the search for optimal solutions, the methodology includes a detailed analysis of various combinatorial optimization problems such as the Traveling Salesman Problem (TSP) and Job-shop Scheduling Problem (JSP). The results demonstrate that ACO, through its ability to simulate pheromone communication and iterative improvements, can effectively yield solutions to complex optimization tasks, positioning it as a valuable tool in fields ranging from graph theory to medical applications. The study underscores the versatility of ACO in addressing multifaceted combinatorial optimization problems, highlighting its relevance across diverse disciplines.
@article{cda7efa1-c3c5-4d3b-9ed4-67139b7779f6,
title={Ant Colony Optimization A Prologue},
author={Gurpreet Singh and Neeraj Kumar},
year={2026},
language={en}
}TY - JOUR TI - Ant Colony Optimization A Prologue AU - Gurpreet Singh AU - Neeraj Kumar PY - 2026 LA - en ER -
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