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Swarm IntelligenceSwarm Intelligence
05005028 (sarat chand)05005029(naresh Kumar)
05005031(veeranjaneyulu)05010033(kalyan raghu)
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.
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
Swarm Intelligence-Definition
“Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems”
Characteristics of Swarms
Composed of many individuals Individuals are homogeneous Local interaction based on simple rules Self-organization
Overview
Ant colony optimization TSP Bees Algorithms Comparison between bees and ants Conclusions
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.
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 of
the other ants.
Ant Colony Optimization
The shorter path will be reinforced by the pheromones further.
Finally , the ants arrive at the shortest path.
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
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
Transitions
Ants leave pheromone trails when they make a transition
Trails are used in prioritizing transition
Transitions
Suppose ant k is 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) with
probability puv(k) = Tuv ( 1/ d(u,v))
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.
Updating Pheromone trails
New trail should have two components Old trail left after evaporation 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
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
Bee Algorithm
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.
Algorithm
It has two steps : ManageBeesActivity() CalculateVectors()
ManageBeesActivity: It handles agents activities based on their internal state. That is it decides action it has to take depending on the knowledge it has.
CalculateVectors : It is used for administrative purposes and calculates PI vectors for the agents.
Uses of Bee Algorithm
Training neural networks for pattern recognition Forming manufacturing cells. Scheduling jobs for a production machine. Data clustering
Comparisons
Ants use pheromones for back tracking route to food 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.
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.
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
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 the
framework of Ant Colony Optimization
BeeHive : another routing algorithm modelled on the communicative behaviour of honey bees
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 sum of parts?
Where does the intelligence of the group come from ?
Answer : Rules followed by individual agents
Conclusion
SI provides heuristics to solve difficult optimization problems.
Has wide variety of applications. Basic philosophy of Swarm Intelligence : Observe
the behaviour of social animals and try to mimic those animals on computer systems.
Basic theme of Natural Computing: Observe nature, mimic nature.
Bibliography
A Bee Algorithm for Multi-Agents System-Lemmens ,Steven . Karl Tuyls, Ann Nowe -2007
Swarm Intelligence – Literature 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.