Swarm Intelligence

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Swarm Intelligence
Swarm 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
  of the 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 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))
  

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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 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

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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 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.

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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 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.

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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 the
  framework of Ant Colony Optimization
  
• BeeHive another routing algorithm modelled on
  the 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
  sum of 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 Observe
  the 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 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.