The document discusses swarm intelligence, defining it as an AI technique inspired by the collective behavior of social insects and decentralized systems. It covers examples of swarm behavior in nature, algorithms like Particle Swarm Optimization and Ant Colony Optimization, and various applications in fields such as robotics and military. Additionally, it highlights advantages and disadvantages of swarm systems, concluding on the potential of mimicking natural behaviors in computational systems.

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