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1. Swarm Intelligence
BugsBusters Research Team

2. Table of contents
• What is meant by Swarm Intelligence?
• Examples in insects life
• PSO and ACO Algorithms
• Applications and Recent Developments
• Advantages and Disadvantages
• Conclusion
• References

3. What is meant by 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” [Bonabeau,
Dorigo, Theraulaz: Swarm
Intelligence] One worker of robot designed as a
worker of ant

4. swarm of robots swarm of Ants

5. Swarm of
birds
Swarm of Flying robots cooperating together

6. What is meant by Swarm
Intelligence? • It is an artificial intelligence (AI) technique based on the
collective behavior in decentralized, self-organized systems
• Generally made up of agents who interact with
each other and the environment
• No centralized control structures
• Based on group behavior found in nature
Agents

7. What is meant by Swarm
Intelligence? • Insects have a few hundred brain cells
• However, organized insects have been known for:
• Architectural marvels
• Complex communication systems
• Resistance to hazards in nature
• In the 1950’s E.O. Wilson observed:
• A single ant acts (almost) randomly – often leading to
its own demise
• A colony of ants provides food and protection for the
entire population

8. Medium Real Ant nests, Taken from the earth

9. • This huge Ant
colony
Concrete, that
has been
Excavated
from earth in
several weeks.
• This Colony
has roads
with shortest
path between
every two
points.

10. What is meant by Swarm
Intelligence? • Characteristics
• Composed of many
individuals
• Individuals are
homogeneous
• Local interaction based
on simple
rules
• Self-organization

11. What is meant by Swarm
Intelligence? • Four Ingredients of Self Organization
• Positive Feedback
• Negative Feedback
• Amplification of Fluctuations –
randomness
• Reliance on multiple interactions

13. Example
• Original Example: Swarm of Bees
• Ant colony
• Agents: ants
• Flock of birds
• Agents: birds
• Traffic
• Agents: cars
• Crowd
• Agents: humans
• Immune system
• Agents: cells and molecules

14. Cont. Example
• Ant Colony
• Every single insect in a social insect colony seems to
have its own agenda, and yet an insect colony looks
so organized.
• The seamless integration of all individual activities does
not seem to require any supervisor.
• For Example there is in one colony different type of
workers:
• Leafcutter Ants
• Weaver Ants
• Army Ants

15. Cont. Examples
• Leafcutter Ants
• cut leaves from
plants and
trees
• Workers forage
for leaves
hundreds of
meters away
from the nest,
• literally
organizing
highways to
and from their
foraging sites

16. Cont. Examples
• Weaver Ants
• workers form chains
of their own bodies,
allowing them to
cross wide gaps and
pull stiff leaf edges
together to form a
nest
• Several chains can
join to form a bigger
one over which
workers run back
and forth.
• Such chains create
enough force to pull
leaf edges together.

17. Cont. Example
• Army Ants
• organize
impressive
hunting raids,
involving up to
200,000 workers,
during which
they collect
thousands of
prey

18. Cont. Examples
• Ant Colony Swarm
benefits:
• Ants forage
better.
• Settle in
organized home.
• Defend it self
against predators
• Social Insects have
survived for millions
of years.

19. Cont. Examples, How to Interact?
• Direct Interactions
• Food/liquid exchange, visual contact, chemical contact
(pheromones)
• Indirect Interactions (Stigmergy)
• Individual behavior modifies the environment, which in
turn modifies the behavior of other individuals
Stigmergy
Example.

20. PSO and ACO Algorithms
• Two Common SI Algorithms
• Ant Colony Optimization
• Particle Swarm Optimization

21. Cont. PSO
• PSO
• A population based stochastic optimization
technique Searches for an optimal solution in
the computable search space.
• Developed in 1995 by Dr. Eberhart and Dr. Kennedy.

22. Cont. PSO
• PSO
• In PSO individuals strive to
improve themselves and
often achieve this by
observing and imitating their
neighbors.
• Each PSO individual has
the ability to remember.
• Inspiration: Swarms of Bees,
Flocks of Birds, Schools of
Fish.

23. Particle Optimization
Technique searching
robots

24. Cont. ACO
• ACO
• Optimization Technique Proposed by Marco Dorigo in the
early ’90
• Heuristic optimization method inspired by biological
systems
• Multi-agent approach for solving difficult combinatorial
optimization problems
• Has become new and fruitful research area

25. Cont. ACO

26. Cont. ACO
• The way ants find their food in shortest path is
interesting.
• Ants secrete pheromones to remember their path.
• These pheromones evaporate with time.
• Whenever an ant finds food , it marks its return journey
with pheromones.

27. Cont. ACO
• Pheromones evaporate faster on longer paths.
(Evaporation)
• Shorter paths serve as the way to food for most of
the other ants.
• The shorter path will be reinforced by the pheromones
further. (Reinforcement)
• Finally , the ants arrive at the shortest path.
(Establishment)

28. Ant Colony Optimization on
Traveling Salesman Pro.

29. Applications and Recent
Developments • Some applications Uses S.I Algorithms :
• Movie effects
• Lord of the Rings
• Network Routing
• ACO Routing
• Swarm Robotics
• Swarm bots

30. Movies
Used
Swarm
Intelligence

31. Cont. Applications and Recent
ODtheerv Reelcoenpt mdeveelnoptesd
• Human tremor analysis
• Human performance assessment
• Ingredient mix optimization

32. Cont. Applications and Recent
ODtheerv Reelcoenpt mdeveelnoptesd
• Evolving neural networks to solve problems
• U.S. Military is applying SI techniques to control of
unmanned vehicles
• NASA is applying SI techniques for planetary mapping
• Medical Research is trying SI based controls for nanobots
to fight cancer

33. Advantages and Disadvantages
• ADVANTAGES:
• The systems are scalable because the same control
architecture can be applied to a couple of agents or
thousands of agents
• The systems are flexible because agents can be easily
added or removed without influencing the structure

34. Advantages and Disadvantages
• ADVANTAGES:
• The systems are robust because agents are simple in
design, the reliance on individual agents is small, and
failure of a single agents has little impact on the
system’s performance
• The systems are able to adapt to new situations easily

35. Cont. Advantages and
Disadvantages • DISADVANTAGES
• Non-optimal – Because swarm systems are highly
redundant and have no central control, they tend to be
inefficient. The allocation of resources is not efficient,
and duplication of effort is always rampant.
• Uncontrollable – It is very difficult to exercise control
over a swarm.

36. Cont. Advantages and
Disadvantages • DISADVANTAGES
• Unpredictable – The complexity of a swarm system leads
to unforeseeable results.
• Non-understandable – Sequential systems are
understandable; complex adaptive systems, instead, are a
jumble of intersecting logic.
• Non-immediate – complex swarm systems with rich
hierarchies take time. The more complex the swarm, the
longer it takes to shift states

37. 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.

38. References
• Reynolds, C. W. (1987) Flocks, Herds, and Schools: A
Distributed Behavioral Model, in Computer Graphics, 21(4)
(SIGGRAPH '87 Conference Proceedings) pages 25-34.
• James Kennedy, Russell Eberhart. Particle Swarm
Optimization, IEEE Conf. on Neural networks – 1995
• www.adaptiveview.com/articles/ ipsop1
• Ruud Schoonderwoerd, Owen Holland, Janet Bruten - 1996.
Ant like agents for load balancing in telecommunication
networks, Adaptive behavior, 5(2) .

39. References
• 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.
• Ant Algorithms for Discrete Optimization Artificial Life
• M.Dorigo, M.Birattari, T.Stutzle, Ant colony optimization –
Artificial Ants as a computational intelligence technique, IEEE
Computational Intelligence Magazine 2006

40. References
• M. Dorigo, G. Di Caro & L. M. Gambardella (1999).
• addr:http://iridia.ulb.ac.be/~mdorigo/
• Swarm Intelligence, From Natural to Artificial Systems
• M. Dorigo, E. Bonabeau, G. Theraulaz
• The Yellowjackets of the Northwestern United States, Matthew Kweskin
• addr:http://www.evergreen.edu/user/serv_res/research/arthropod/TESCBiota/Vespi
dae/Kweskin97/main.htm
• Entomology & Plant Pathology, Dr. Michael R. Williams
addr: http://www.msstate.edu/Entomology/GLOWORM/GLOW1PAGE.html
• Urban Entomology Program, Dr. Timothy G. Myles
addr:http://www.utoronto.ca/forest/termite/termite.htm

41. References
• Dorigo, Marco and Stützle, Thomas. (2004) Ant Colony Optimization,
Cambridge, MA: The MIT Press.
• Dorigo, Marco, Gambardella, Luca M., Middendorf, Martin. (2002)
“Guest Editorial,” IEEE Transactions on Evolutionary Computation, 6(4):
317-320.
• Ant Colony Optimization by Marco Dorigo and Thomas Stϋtzle, The MIT
Press, 2004
• Swarm Intelligence by James Kennedy and Russell Eberhart with Yuhui
Shi, Morgan Kauffmann Publishers, 2001
• Advances in Applied Artificial Intelligence edited by John Fulcher, IGI
Publishing, 2006
• Data Mining: A Heuristic Approach by Hussein Abbass, Ruhul Sarker,
and Charles Newton, IGI Publishing, 2002