This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.

1. Ant Colony
Optimization
Mohamed Talaat
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2. Agenda
• What is ACO?
• Behaviors of Real ANTs
• Basic Idea of ACO.
• ACO Algorithm.
• Demo
• Applications of ACO.
• Advantages of ACO.
• Limitations of ACO.
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What is ACO?
• Search technique to calculate a shortest path
between the source and destination. Biologically-
inspired from the behavior of natural ANTs.
• Ant system was developed by Marco Dorigo (Italy) in
his PhD thesis in 1992.

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Behaviors of Real ANTs
• Regulation of nest temperature;
• Forming bridges;
• Raiding specific areas for food;
• Building and protecting nest;
• Sorting brood and food items;
• Cooperating in carrying large items;
• Emigration of a colony;
• Finding shortest route from nest to food source;
• Preferentially exploiting the richest food source available.
The ACO algorithm is inspired by this:

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Stigmergy
• Stigmergy is indirect communication via interaction
with the environment.
• Ants have highly developed sophisticated sign-based
Stigmergy:
– They communicate using pheromones.
– They lay trails of pheromone that can be
followed by other ants.

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Basic Idea Of ACO
• 2 ants start with equal probability of
going on either path.

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Basic Idea Of ACO
• The ant on shorter path has a shorter
to-and-from time from it’s nest to the
food.

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Basic Idea Of ACO
• The density of pheromone on the shorter path
is higher because of 2 passes by the ant (as
compared to 1 by the other).

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Basic Idea Of ACO
• The next ant takes the shorter route.

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Basic Idea Of ACO
• Over many iterations, more ants begin using
the path with higher pheromone, thereby
further reinforcing it.

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Basic Idea Of ACO
• After some time, the shorter path is almost
exclusively used.

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Basic Idea Of ACO
• The first ant wonders randomly until it finds its food source (F),
then it return to the nest (N), laying a pheromone trail.
• Other ants follow one on of the paths at random, also laying
pheromone trails.
• The ant on the shortest path lay pheromone trails faster, making
it more appearing to future ants.
• The ants become increasingly likely to follow this shortest path.
• The pheromone trails of the longer paths evaporate.
Shortest path is discovered via pheromone trails.

13. ACO Algorithm
• At the beginning of the search process, a constant amount of
pheromone is assigned to all arcs. When located at a node i an ant k use
the pheromone trail to compute the probability of choosing j as the next
node:

14. ACO Algorithm
• When the arc (i,j) is traversed, the pheromone value changes as follows:

15. ACO Algorithm

16. Demo
TSP – ACOhttp://www.theprojectspot.com/downloads/tsp-aco.html
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Applications of ACO
• Routing in telecommunication networks.
• Traveling Salesman.
• Graph Coloring
• Scheduling
• Vehicle routing.

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Advantage/Disadvantages s of ACO
• Advantages:
• Simple implementation.
• Easily parallelized for concurrent processing.
• Derivative free.
• Efficient for TSP and similar problems.
• Disadvantages:
• Probability distribution changes by iteration.
• Time to convergence uncertain (but convergence is
guaranteed!)

19. Thanks 
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