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Swarm Intelligence. Swarm 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?

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swarm intelligence
Swarm Intelligence

Swarm Intelligence

05005028 (sarat chand)05005029(naresh Kumar)05005031(veeranjaneyulu)05010033(kalyan raghu)‏

  • 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
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-Definition
  • “Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems”
characteristics of swarms
Characteristics of Swarms
  • Composed of many individuals
  • Individuals are homogeneous
  • Local interaction based on simple rules
  • Self-organization
  • Ant colony optimization
  • TSP
  • Bees Algorithms
  • Comparison between bees and ants
  • Conclusions
ant colony optimization
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 optimization1
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 optimization2
Ant Colony Optimization
  • The shorter path will be reinforced by the pheromones further.
  • Finally , the ants arrive at the shortest path.
optimization using si
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
  • Ants leave pheromone trails when they make a transition
  • Trails are used in prioritizing transition
  • 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
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
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
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
bees foraging
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.
  • 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
Uses of Bee Algorithm
  • Training neural networks for pattern recognition
  • Forming manufacturing cells.
  • Scheduling jobs for a production machine.
  • Data clustering
  • 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.
  • 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
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
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
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
  • 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.
  • 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.