Bio-inspired Ant Algorithms: A review

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Sangita Roy 1,* Sheli Sinha Chaudhuri 2

1. Department of Electronics and Communication Engineering, Narula Institute of Technology, WBUT

2. Department of Electronics and Telecommunication Engineering, Jadavpur University, India

* Corresponding author.


Received: 5 Jan. 2013 / Revised: 11 Feb. 2013 / Accepted: 12 Mar. 2013 / Published: 8 Apr. 2013

Index Terms

Stigmergic, Forage, Pheromone, Combitorial Optimisation, GA, Artificial Ant, AI.


Ant Algorithms are techniques for optimizing which were coined in the early 1990’s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. The focus of ant algorithms is to find approximate optimized problem solutions using artificial ants and their indirect decentralized communications using synthetic pheromones. In this paper, at first ant algorithms are described in details, then transforms to computational optimization techniques: the ACO metaheuristics and developed ACO algorithms. A comparative study of ant algorithms also carried out, followed by past and present trends in AAs applications. Future prospect in AAs also covered in this paper. Finally a comparison between AAs with well-established machine learning techniques were focused, so that combining with machine learning techniques hybrid, robust, novel algorithms could be produces for outstanding result in future.

Cite This Paper

Sangita Roy, Sheli Sinha Chaudhuri, "Bio-inspired Ant Algorithms: A review", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.4, pp. 25-35, 2013. DOI:10.5815/ijmecs.2013.04.04


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