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Routing Using Ant Colony
Optimization
By
Bharath R
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Contents
Introduction to swarm
intelligence
Behavior of ants
Introduction to antcolony optimization
Double bridge
experiment Ant moves
Applications
Why Ant colony
optimization
General system
Routing
Selecting next nodes
Pheromone update
Algorithm Disadvantages
Conclusion
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Introduction to Swarm Intelligence
Swarm intelligence (SI) is the collectivebehavior of decentralized, self-organized systems, naturalor artificial.
Achieving a collective performance which could not
normally be achieved by an individual acting alone
SI systems are typically made up of a population ofsimple agents interacting locally with one another andwith their environment. The inspiration often comes
from nature, especially biological systems. examples of SI include ant colonies, bird flocking,
animal herding, bacterial growth, and fish schooling.
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Natural Behavior of Ant
Ant colonies can collectively perform tasks and make
decisions that appear to require a high degree of co-
ordination among the workers: building a nest, feeding
the brood, foraging for food, and so on.
Ants (initially) wander randomly, and upon finding food
return to their colony while laying
down pheromone trails.
If other ants find such a path, they are likely not to keeptravelling at random, but to instead follow the trail,
returning and reinforcing it if they eventually find food
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Ant Colony Optimization
The ant colony optimization algorithm (ACO) is
a probabilistic technique for solving computational
problems which can be reduced to finding good pathsthrough graphs.
The original idea comes from observing the exploitation
of food resources among ants, in which ants individually
limited cognitive abilities have collectively been able tofind the shortest path between a food source and the
nest.
Introduction
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Double Bridge Experiment
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Ant Moves
Four types:
From home to food
Goal has never been reached
Goal reached
Back to home
Goal has never been reached
Goal reached
Idea: generates several random moves and see which one isthe best among them.
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Applications
Traveling Salesman Problem
Quadratic Assignment Problem
Network Routing Problem
Vehicle routing
Scheduling
Telecommunication Network
Graph Coloring Water Distribution Network
etc . . .
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Why ACO?
Positive Feedback accounts for rapid discovery of good
solutions
Distributed computation avoids premature convergence
The greedy heuristic helps find acceptable solution in the
early solution in the early stages of the search process.
The collective interaction of a population of agents.
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General System
Virtual trail accumulated on path segments
Starting node selected at random
Path selected at random
Based on amount of trail present on possible paths fromstarting node
higher probability for paths with more trail
Ant reaches next node, selects next path
Continues until reaches starting node Finished tour is a solution
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General System
A completed tour is analyzed for optimality
Trail amount adjusted to favor better solutions
better solutions receive more trail
worse solutions receive less trail
higher probability of ant selecting path that is part of
a better-performing tour
New cycle is performed
Repeated until most ants select the same tour on every
cycle (convergence to solution)
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ACO in Routing
Dynamic Routing
At any moment the pathway of a message must be as
small as possible. (Traffic conditions and the structure of
the network are constantly changing)
Load balancing
Distribute the changing load over the system and minimize
lost calls.
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Routing
There are two working modes for the ants: either
forwards or backwards.
Pheromones are only deposited in backward mode.
The ants memory allows them to retrace the path it hasfollowed while searching for the destination node
Before moving backward on their memorized path, they
eliminate any loops from it. While moving backwards,
the ants leave pheromones on the arcs they traversed.
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Routing(cont..)
The ants evaluate the cost of the paths they have
traversed.
The shorter paths will receive a greater deposit of
pheromones. An evaporation rule will be tied with thepheromones, which will reduce the chance for poor
quality solutions.
The ants evaluate the cost of the paths they have
traversed. The shorter paths will receive a greaterdeposit of pheromones.
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Choosing the next node
When located at a node i an ant k uses the pheromone
trail to compute the probability of choosing j as the next
node:
is the intensity of the trail on edge (i,j) at time t.
is the inverse of distance from i to j called visibility.
and are parameters that controls the relative
importance of the pheromone versus the heuristic
information
ij
k
i
Nl ijij
ijijk
ij Njift
t
tpki
][)]([][)]([
)(
ij
ij
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Choosing next node(cont..)
In case of ACO, the next node is selected dynamically and
randomly, with the probability to choose the shortest
path more, so some of the packets may follow some
other path which increases the net throughput of the
network when the number of packets in the network
increases.
Here the roulette wheel selection method can be used to
select the next node in the path.
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Routing Wheel Selection
The individuals are mapped to contiguous segments of a
line, such that each individual's seg-ment is equal in size
to its fitness.
A random number is generated and the individual whosesegment spans the random number is selected.
Example
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Updating Pheromone
When the arc (i,j) is traversed , the pheromone value
changes as follows:
is the evaporation rate
m is the total number of successful ants(packets).
is the quantity of pheromone laid on edge (i,j) by
packet k, typically given by
= 1/Lk
ij
k
i
k
ijijij
1
)1(
k
ij
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Algorithm
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Algorithm cont..
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Disadvantages
Tradeoffs in evaluating convergence:
In NP-hard problems focus is on quality of solutions
In dynamic network routing problems, need solutions for
changing conditions focus is on effective evaluation of
alternative paths
Coding is somewhat complicated, not straightforward
Pheromone trail additions/deletions, global updates and local
updates
Large number of different ACO algorithms to exploit different
problem characteristics
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Conclusion
ACO is well defined and good performing and
metaheuristic that is more and more often applied to
solve a variety of complex combinatorial problems.
The existing system has been defined and some results
have been summarized.
Recent trends have been discussed.
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References
Dorigo, Marco and Sttzle, Thomas. (2004) Ant ColonyOptimization, Cambridge, MA: The MIT Press.
International Journal of Computer Applications (0975 -8887) Volume 1 No. 15 by Debasmita Mukherjee and
Sriyankar Acharyya. 2011 IEE computational Intelligence Magazine I November
2006
http://en.wikipedia.org/wiki/Ant_colony_optimization_al
gorithms Evolutionary Algorithms: Overview, Methods and
Operators by: Hartmut Pohlheim
http://en.wikipedia.org/wiki/Ant_colony_optimization_algorithmshttp://en.wikipedia.org/wiki/Ant_colony_optimization_algorithmshttp://en.wikipedia.org/wiki/Ant_colony_optimization_algorithmshttp://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms