Vol. 3 No. 1 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Ant Colony Optimization - Models and Applications

Dr. Sofia Costa
Professor, AI in Healthcare Decision Making, Lisbon Institute of Technology, Lisbon, Portugal
Cover

Published 16-04-2023

Keywords

  • Ant Colony Optimization,
  • Metaheuristic,
  • Combinatorial Optimization,
  • Ant System

How to Cite

[1]
Dr. Sofia Costa, “Ant Colony Optimization - Models and Applications”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 1, pp. 23–29, Apr. 2023, Accessed: Nov. 13, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/6

Abstract

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants. This paper provides a comprehensive overview of ACO models and their applications in solving combinatorial optimization problems. The paper begins by introducing the concept of ACO and its underlying principles. It then discusses various ACO algorithms, including Ant System, Ant Colony System, and Max-Min Ant System, highlighting their key features and differences. The paper also explores the use of ACO in solving a wide range of combinatorial optimization problems, such as the Traveling Salesman Problem, the Vehicle Routing Problem, and the Quadratic Assignment Problem. Additionally, the paper examines recent advancements in ACO, such as hybrid approaches and parallel implementations, and discusses future directions in ACO research. Overall, this paper serves as a comprehensive guide to understanding ACO models and their applications in solving complex optimization problems.

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