Ant colony algorithm is an algorithm inspired by how ants find food. It analyzes how ants move between their colony and a food source to develop a mathematical model. Ants tend to find the shortest path between two points in a systematic rather than random way. Individual ants leave and follow pheromone trails, an unseen chemical they secrete. Over time, the strongest pheromone trails that indicate the shortest paths emerge, organizing the initially random search. The ant colony algorithm can be modeled computationally and applied to optimization problems by simulating this behavior.

1. Prepared by:
Qusai Nsour
CS master – Artificial inelegant course.
Supervised by Dr. Khalaf Khatatneh.
Summer semester- 2016

2. ‫تعالى‬ ‫قال‬:

3. Definition
• Ant colony algorithm is an algorithm that
depends on analyzing the way ants move to
find there food, and conclude a mathematical
form that could be applied in solving problems
that mimic the ants when travelling between
point A to B or “ 2 nodes”.

4. Why ants?
• A deep look into the way ants move in finding
there way indicates that there is a systematic
– not random – way when they move
between there colony and food ( 2 nodes).
• Ants tend to use the shortest path between
the two nodes.
• If there happened to be an obstacle in there
way, ants learn how to avoid it.

5. Historical look

6. The ants trick
• In fact there is no trick behind this, the ants
depends on there smelling sense, they follow
the smell that is left on the way to the food by
a leading “agent”.
• This smell is generated by a “pheromone” :
A pheromone is a unseen or excreted
chemical factor that triggers a social response
in members of the same species.

7. Organizing randomness
• Ants start searching for food in a random way,
there is no clue to where the food is.

8. Organizing randomness
There graph is generated in a random way
“what does this look like?!!”
colony
food

9. Organizing randomness
Eliminating Nodes
By finding where the smell of the pheromone is
stronger

10. Organizing randomness
Determining shortest path

11. Here comes the AI …!
• We have a multi-agent system
• A learning techniques
• Factors that define those techniques
• We have source and destination or shall we
say “a game”…!
• Can we build a computational model for this
system … if yes … congratulations … this
means that we can program it…!!!

12. The math

13. The math

14. Main ant colony alg.

15. Applications

16. pseudo code for ant algorithm

17. refrences
• https://en.wikipedia.org/wiki/Main_Page
• Dorigo, Marco, and Luca Maria Gambardella.
"Ant colony system: a cooperative learning
approach to the traveling salesman
problem."Evolutionary Computation, IEEE
Transactions on 1.1 (1997): 53-66.
• Anirudeh, et, Artificial intelligent seminar,
Indian institute of technology.

18. • Any Questions ?