Ant Colony Optimization (ACO) is a biologically-inspired search technique for finding the shortest path, developed by Marco Dorigo in 1992. It mimics ant behaviors such as pheromone communication and path selection to converge on optimal solutions for problems like routing and scheduling. While ACO has advantages like simplicity and efficiency, it also faces challenges with time to convergence and changing probability distributions.

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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3. 3
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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