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. 21, 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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References

  1. Venigandla, Kamala, and Venkata Manoj Tatikonda. "Improving Diagnostic Imaging Analysis with RPA and Deep Learning Technologies." Power System Technology 45.4 (2021).
  2. Pillai, Aravind Sasidharan. "A Natural Language Processing Approach to Grouping Students by Shared Interests." Journal of Empirical Social Science Studies 6.1 (2022): 1-16.
  3. Dixit, Rohit R. "Factors Influencing Healthtech Literacy: An Empirical Analysis of Socioeconomic, Demographic, Technological, and Health-Related Variables." Applied Research in Artificial Intelligence and Cloud Computing 1.1 (2018): 23-37.
  4. Schumaker, Robert, et al. "An Analysis of Covid-19 Vaccine Allergic Reactions." Journal of International Technology and Information Management 30.4 (2021): 24-40.
  5. Elath, Harshini, et al. "Predicting Deadly Drug Combinations through a Machine Learning Approach." PACIS. 2018.
  6. Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.
  7. Ravi, Kiran Chand, et al. "AI-Powered Pancreas Navigator: Delving into the Depths of Early Pancreatic Cancer Diagnosis using Advanced Deep Learning Techniques." 2023 9th International Conference on Smart Structures and Systems (ICSSS). IEEE, 2023.
  8. Dixit, Rohit R., Robert P. Schumaker, and Michael A. Veronin. "A Decision Tree Analysis of Opioid and Prescription Drug Interactions Leading to Death Using the FAERS Database." IIMA/ICITED Joint Conference 2018. INTERNATIONAL INFORMATION MANAGEMENT ASSOCIATION, 2018.
  9. Reddy, Surendranadha Reddy Byrapu. "Enhancing Customer Experience through AI-Powered Marketing Automation: Strategies and Best Practices for Industry 4.0." Journal of Artificial Intelligence Research 2.1 (2022): 36-46.
  10. Raparthi, Mohan, et al. "Data Science in Healthcare Leveraging AI for Predictive Analytics and Personalized Patient Care." Journal of AI in Healthcare and Medicine 2.2 (2022): 1-11.