Jyotishkar dey roll 36.(swarm intelligence)

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This was my presentation on Swarm Intelligence in my college

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Jyotishkar dey roll 36.(swarm intelligence)

  1. 1. Swarm Intelligence Presented by: JYOTISHKAR DEY ROLL-36 From Natural to Artificial Systems
  2. 2. Swarm • Swarm is a collection of agents interacting locally with one another and with their environment.
  3. 3. Examples • A flock of birds flying together in sky for search of food • A population of ant in search of nectar • A school of dolphins on their journey of migration
  4. 4. Swarm Intelligence • Definition:-“Any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies “ • Computer scientists are increasing interested in swarm intelligence since it can be used to solve many optimization problems. • Well-defined, but computational hard problems (NP hard problems )can be solved (eg:Travelling Salesman Problem)
  5. 5. Real World Insect Examples
  6. 6. BEES Bees collecting nectar collaboratively
  7. 7. BIRDS Birds flying together
  8. 8. Collision Avoidance
  9. 9. Velocity Matching Rule 2: Match the velocity of neighboring birds
  10. 10. Flock Centering • Rule 3: Stay near neighboring birds
  11. 11. Characteristics of Swarms • Composed of many individuals • Individuals are homogeneous • Local interaction based on simple rules • Self-organization
  12. 12. ANT COLONY OPTIMIZATION • Cooperative search by pheromone trails
  13. 13. Starting journey ANT COLONY OPTIMIZATION
  14. 14. During return journey ant leaves behind traces of pheromones ANT COLONY OPTIMIZATION
  15. 15. The ant following shortest path return first. The next ant smells its pheromons and probability of it chosing this shortest path increases. ANT COLONY OPTIMIZATION
  16. 16. ANT COLONY OPTIMIZATION
  17. 17. Final Reinforced shortest path. ANT COLONY OPTIMIZATION
  18. 18. Transitions • Suppose ant k is at u. • Nk(v) 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(v) with probability puv(k) = Tuv ( 1/ d(u,v))
  19. 19. Application of ANT colony optimization • Travelling salesman problem • Shortest route • Congestion • Flexibility
  20. 20. New Shortest path(Flexibility)
  21. 21. Bee Algorithm
  22. 22. • The foraging process begins in a colony by scout bees being sent to search for promising flower patches. Scout bees move randomly from one patch to another. During the harvesting season, a colony continues its exploration, keeping a percentage of the population as scout bees. • When they return to the hive, those scout bees that found a patch which is rated above a certain quality threshold (measured as a combination of some constituents, such as sugar content) deposit their nectar or pollen and go to the “dance floor” to perform a dance known as the waggle dance
  23. 23. • This dance is essential for colony communication, and contains three pieces of information regarding a flower patch: the direction in which it will be found, its distance from the hive and its quality rating (or fitness). This information helps the colony to send its bees to flower patches precisely, without using guides or maps. • After waggle dancing inside the hive, the dancer (i.e. the scout bee) goes back to the flower patch with follower bees that were waiting inside the hive. More follower bees are sent to more promising patches. This allows the colony to gather food quickly and efficiently. • While harvesting from a patch, the bees monitor its food level. This is necessary to decide upon the next waggle dance when they return to the hive. If the patch is still good enough as a food source, then it will be advertised in the waggle dance and more bees will be recruited to that source.
  24. 24. PRACTICAL APPLICATIONS OF SWARM INTELLIGENCE
  25. 25. ROBOTS • Decentralised control • Local Information • Anonymity
  26. 26. Communication Networks • Routing packets to destination in shortest time • Similar to Shortest Route • Statistics kept from prior routing (learning from experience)
  27. 27. Antifying Website Searching • Digital-Information Pheromones (DIPs) • Ant World Server
  28. 28. APPLICATIONOF SI IN MANET • Mobile Ad-Hoc Networks (referred to as MANETs), are wireless communication networks . • An ideal application is for search and rescue operations. Such scenarios are characterized by the lack of installed communications infrastructure. This may be because all of the equipment was destroyed, or perhaps because the region is too remote. Rescuers must be able to communicate in order to make the best use of their energy, but also to maintain safety. By automatically establishing a data network with the communications equipment that the rescuers are already carrying, their job made easier.singly appearing in the Commercial, Military, and Private sector.
  29. 29. Advantages • Highly Scalable • Adaptability to changing environment making use of self organizing capability • Highly robust because they don’t have single point of failure. • Individual Simplicity-Simple individual elements with limited capability having simple behavorial rules can be used to solve complicated problems.
  30. 30. Disadvantages • Unsuitable for Time-Critical Applications: Because the pathways to solutions in SI systems are not predifined the time of convergence is unknown. • Stagnation: Because of the lack of central coordination, SI systems could suffer from a stagnation situation or a premature convergence to a local optimum
  31. 31. The Future?
  32. 32. 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.
  33. 33. Thank you

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