Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.

1. Swarm Intelligence
Presented by:
JYOTISHKAR DEY
ROLL-36
From Natural to Artificial Systems

2. Swarm
• Swarm is a collection of agents
interacting locally with one another and
with their environment.

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. 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. Real World
Insect
Examples

6. BEES
Bees collecting nectar collaboratively

7. BIRDS
Birds flying together

8. Collision Avoidance

9. Velocity Matching
Rule 2: Match the velocity of neighboring
birds

10. Flock Centering
• Rule 3: Stay near neighboring birds

11. Characteristics of Swarms
• Composed of many individuals
• Individuals are homogeneous
• Local interaction based on simple rules
• Self-organization

12. ANT COLONY
OPTIMIZATION
• Cooperative search by pheromone trails

13. Starting journey
ANT COLONY
OPTIMIZATION

14. During return journey ant leaves behind traces of
pheromones
ANT COLONY
OPTIMIZATION

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. ANT COLONY
OPTIMIZATION

17. Final Reinforced shortest path.
ANT COLONY
OPTIMIZATION

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. Application of ANT colony
optimization
• Travelling salesman problem
• Shortest route
• Congestion
• Flexibility

20. New Shortest path(Flexibility)

21. Bee Algorithm

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

25. • 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.

26. PRACTICAL
APPLICATIONS OF
SWARM
INTELLIGENCE

27. ROBOTS
• Decentralised control
• Local Information
• Anonymity

28. Communication Networks
• Routing packets to destination in
shortest time
• Similar to Shortest Route
• Statistics kept from prior routing
(learning from experience)

29. Antifying Website Searching
• Digital-Information Pheromones
(DIPs)
• Ant World Server

30. 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.

31. 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.

32. 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

33. The Future?

34. 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.

35. Thank you