Swarm Intelligence

1. Swarm Intelligence Swarm Intelligence 05005028 (sarat chand)05005029(naresh Kumar)05005031(veeranjaneyulu)05010033(kalyan raghu)‏

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. 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

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-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.

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

17. Bee Algorithm

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.