routring using ant colony optimization

7/30/2019 Routring Using Ant Colony Optimization

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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

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