group 12 swarmintelligence

7/28/2019 Group 12 SwarmIntelligence

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Swarm IntelligenceSwarm Intelligence

05005028 (sarat chand)05005029(naresh Kumar)05005031(veeranjaneyulu)

05010033(kalyan raghu)


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Swarms

Natural phenomena as inspiration

A flock of birds sweeps across the Sky.

How do ants collectively forage for food?

How does a school of fish swims, turns together? They are so ordered.


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What made them to be so ordered?

There is no centralized controller

But they exhibit complex global behavior.

Individuals follow simple rules to interact with

neighbors . Rules followed by birds

collision avoidance

velocity matching

Flock Centering


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Swarm Intelligence-Definition

Swarm intelligence (SI) is artificial intelligence

based on the collective behavior of decentralized,

self-organized systems


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Characteristics of Swarms

Composed of many individuals

Individuals are homogeneous

Local interaction based on simple rules

Self-organization


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Overview

Ant colony optimization

TSP

Bees Algorithms

Comparison between bees and ants Conclusions


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Ant Colony Optimization

The way ants find their food in shortest path is

interesting.

Ants secrete pheromones to remember their path.

These pheromones evaporate with time.


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Ant Colony Optimization..

Whenever an ant finds food , it marks its return

journey with pheromones.

Pheromones evaporate faster on longer paths.

Shorter paths serve as the way to food for most ofthe other ants.


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Ant Colony Optimization

The shorter path will be reinforced by the

pheromones further.

Finally , the ants arrive at the shortest path.


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Optimization using SI

Swarms have the ability to solve problems

Ant Colony Optimization (ACO) , a meta-heuristic

ACO can be used to solve hard problems like TSP,

Quadratic Assignment Problem(QAP) We discuss ACO meta-heuristic for TSP


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ACO-TSP

Given a graph with n nodes, should give the

shortest Hamiltonian cycle

m ants traverse the graph

Each ant starts at a random node


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Transitions

Ants leave pheromone trails when they make a

transition

Trails are used in prioritizing transition


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Transitions

Suppose ant kis at u.

Nk(u) be the nodes not visited by k

Tuv be the pheromone trail of edge (u,v)

k jumps from u to a node v in Nk(u) withprobability

puv(k) = Tuv ( 1/ d(u,v))


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Iteration of AOC-STP

m ants are started at random nodes

They traverse the graph prioritized on trails and

edge-weights

An iteration ends when all the ants visit all nodes After each iteration, pheromone trails are updated.


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Updating Pheromone trails

New trail should have two components Old trail left afterevaporation and

Trails added by ants traversing the edge during the

iteration T'uv = (1-p) Tuv + ChangeIn(Tuv)

Solution gets better and better as the number of

iterations increase


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Performance of TSP with ACO heuristic

Performs better than state-of-the-art TSP

algorithms for small (50-100) of nodes

The main point to appreciate is that Swarms give

us new algorithms for optimization


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Bee Algorithm


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Bees Foraging

Recruitment Behaviour : Waggle Dancing

series of alternating left and right loops

Direction of dancing

Duration of dancing

Navigation Behaviour : Path vector represents knowledge representation of

path by inspect

Construction of PI.


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Algorithm

It has two steps : ManageBeesActivity()

CalculateVectors()

ManageBeesActivity: It handles agents activitiesbased on their internal state. That is it decides

action it has to take depending on the knowledge it

has.

CalculateVectors : It is used for administrativepurposes and calculates PI vectors for the agents.


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Uses of Bee Algorithm

Training neural networks for pattern recognition

Forming manufacturing cells.

Scheduling jobs for a production machine.

Data clustering


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Comparisons

Ants use pheromones for back tracking route tofood source.

Bees instead use Path Integration. Bees are able to

compute their present location from past trajectory

continuously. So bees can return to home through direct route

instead of back tracking their original route.

Does path emerge faster in this algorithm.


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Results

Experiments with different test cases on these

algorithms show that. Bees algorithm is more efficient when finding and

collecting food, that is it takes less number of steps. Bees algorithm is more scalable it requires less

computation time to complete task.

Bees algorithm is less adaptive than ACO.


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Applications of SI

In Movies : Graphics in movies like Lord of the

Rings trilogy, Troy.

Unmanned underwater vehicles(UUV):

Groups of UUVs used as security units Only local maps at each UUV Joint detection of and attack over enemy vessels by co-

ordinating within the group of UUVs


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More Applications

Swarmcasting: For fast downloads in a peer-to-peer file-sharing

network Fragments of a file are downloaded from different

hosts in the network, parallelly.

AntNet : a routing algorithm developed on theframework of Ant Colony Optimization

BeeHive : another routing algorithm modelled onthe communicative behaviour of honey bees


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A Philosophical issue

Individual agents in the group seem to have no

intelligence but the group as a whole displays

some intelligence

In terms of intelligence, whole is not equal to sumof parts?

Where does the intelligence of the group come

from ?

Answer : Rules followed by individual agents


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Conclusion

SI provides heuristics to solve difficult

optimization problems.

Has wide variety of applications.

Basic philosophy of Swarm Intelligence : Observethe behaviour of social animals and try to mimic

those animals on computer systems.

Basic theme of Natural Computing: Observe

nature, mimic nature.


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Bibliography

A Bee Algorithm for Multi-Agents System-

Lemmens ,Steven . Karl Tuyls, Ann Nowe -2007

Swarm IntelligenceLiterature Overview, Yang

Liu , Kevin M. Passino. 2000. www.wikipedia.org

The ACO metaheuristic: Algorithms, Applications,

and Advances. Marco Dorigo and Thomas Stutzle-

Handbook of metaheuristics, 2002.
http://www.wikipedia.org/http://www.wikipedia.org/http://www.wikipedia.org/

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