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Swarm Intelligence
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.
Optimizations of 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-TSP
 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.

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Meta Heuristics Optimization and Nature Inspired.ppt

  • 2. 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.
  • 3.  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
  • 4. Swarm Intelligence-Definition  “Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems”
  • 5. Characteristics of Swarms  Composed of many individuals  Individuals are homogeneous  Local interaction based on simple rules  Self-organization
  • 6. Overview  Ant colony optimization  TSP  Bees Algorithms  Comparison between bees and ants  Conclusions
  • 7. 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.
  • 8. 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.
  • 9. Ant Colony Optimization  The shorter path will be reinforced by the pheromones further.  Finally , the ants arrive at the shortest path.
  • 10. Optimizations of 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
  • 11. 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
  • 12. Transitions  Ants leave pheromone trails when they make a transition  Trails are used in prioritizing transition
  • 13. 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))
  • 14. Iteration of AOC-TSP  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.
  • 15. 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
  • 16. 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
  • 18.
  • 19. 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.
  • 20. 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.
  • 21. Uses of Bee Algorithm  Training neural networks for pattern recognition  Forming manufacturing cells.  Scheduling jobs for a production machine.  Data clustering
  • 22. 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.
  • 23. 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.
  • 24. 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
  • 25. 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
  • 26. 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
  • 27. 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.
  • 28. 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.