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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/