Ant colonies, and more generally social insect societies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases far exceed the individual capacities of a single ant. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing. They are also able to adapt to changes in the environment. “Ant Colony Optimization” is an algorithm which searches for the solution of the problem under consideration in the way similar to real ants. It tries to make use of real ant abilities to solve various optimization problems. In this report study of simple ant algorithms has been done. Also, as an example they are applied on famous Traveling Salesman Problem. Finally, some results are tabulated comparing these algorithms with other optimization heuristics.
[1]
Marco Dorigo,et al.
Distributed Optimization by Ant Colonies
,
1992
.
[2]
Michele Lanzetta,et al.
Ant Colony Optimization
,
2012
.
[3]
Riccardo Poli,et al.
New ideas in optimization
,
1999
.
[4]
Marco Dorigo,et al.
Ant algorithms and stigmergy
,
2000,
Future Gener. Comput. Syst..
[5]
Luca Maria Gambardella,et al.
5 Ant Colony Optimization
,
2004
.
[6]
M. Dorigo,et al.
Ant System: An Autocatalytic Optimizing Process
,
1991
.
[7]
J. Deneubourg,et al.
The self-organizing exploratory pattern of the argentine ant
,
1990,
Journal of Insect Behavior.
[8]
M Dorigo,et al.
Ant colonies for the travelling salesman problem.
,
1997,
Bio Systems.
[9]
Luca Maria Gambardella,et al.
Ant Algorithms for Discrete Optimization
,
1999,
Artificial Life.