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Ant Colony Optimization.

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Proposed by Marco Dorigo et al. in 1990s

Inspired by the social behaviours of ant colonies

Main application: Network Optimisation

Background.

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Can explore vast areas without global view of the ground.

Can find the food and bring it back to the nest.

Will converge to the shortest path.

The Ants.

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By leaving pheromones behind them.

Wherever they go, they let pheromones behind here, marking the area as explored and communicating to the other ants that the way is known.

Double Bridge experiment

How can they manage such great tasks ?

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The more ants follow a trail, the more attractive that trail becomes for being followed.

Double Bridge Experiment

NEST FOODNEST FOODNEST FOOD

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Ants are forced to decide whether they should go left or right, and the choice that is made is a random decision.

Pheromone accumulation is faster on the shorter path.

The difference in pheromone content between the two paths over time makes the ants choose the shorter path.

Different optimization problems have been explored using a simulation of this real ant behavior.

Route Selection.

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Traveling Salesman Problem.◦ A salesman has to visit ‘n’

towns cyclically.

◦ In one tour he visits each towns just once and finishes up where he started.

In what order should he visit them to minimise the distance travelled?

◦ How many orderings? ( n=21)

20!= 20x19x18x17……3x2x1

=2,432,902,008,176,640,000

This number is so big that if the computer could

check 1 billion orderings every second it would still

take 77 years to check them all!

So, we need clever algorithms to solve it.

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Problem Definition.

OBJECTIVE:Given a set of ‘n’ cities, the

Traveling

Salesman Problem requires a

salesman to find the shortest route

between the given cities and return

to the starting city, while keeping in

mind that each city can be visited

only once.

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Flowchart of ACO

Have all cities been

visited

Have the maximum

Iterations been performed

START ACO

Locate ants randomly in cities across the grid and store the

current city in a tabu list

Determine probabilistically as to which city to visit next

Move to next city and place this city in the

tabu list

Record the length of tour and clear tabu list

Determine the shortest tour till now and

update pheromone

NO

YES

STOPACO

YESNO

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Lots of practical applications

Routing such as in trucking, delivery, UAVs

Manufacturing routing such as movement of parts along manufacturing floor or the amount of solder on circuit board

Network design such as determining the amount of cabling required

TSP Applications.

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Questions