Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.

1. Introduction to
Swarm Intelligence
What can we learn from natural intelligence?
Venkatesh Vinayakarao
Rekha Tokas
Haroon Rashid
(In the order of presentation)

2. Swarms
Loosely connected
interactive agents
Collective
behavior
Swarms
• Display collective behavior
• Have loosely connected
interactive agents
Why Swarm?
• Seek food
• Migrate
• Defense (even attack)
2

3. Concepts
 Stigmergy – Stimulation by work
 No direct communication
 Agents react to environment
 work that does not depend on specific agents
 Eg
 Ant colonies find shortest path to food and maximum distance
from colony entrances to dispose dead ants (midden piles).
 Queen ant reproduces. Worker ants (of whom, queen is the
mother), fetch food and dispose waste.
 Queen does not give any orders. Queen has no authority or
decision making control.
 Emergence
 Complex patterns from simple interactions
Eg., structures of termite colonies
 Self Organization
 Feedback Oriented
 Multiple Interactions based on simple rules
3

4. What is Swarm Intelligence?
 “any attempt to design algorithms or distributed
problem-solving devices inspired by the collective
behavior of social insect colonies and other animal
societies” [Bonabeau etal.]
 Swarm
 A loosely structured collection of interacting agents
 Agents:
 Individuals that belong to a group (but are not necessarily identical)
 They contribute to and benefit from the group
 They can recognize, communicate, and/or interact with each other
 The instinctive perception of swarms is a group of agents in
motion – but that does not always have to be the case.
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5. Understanding Swarm Behavior
• How do these aggregations work?
• Selfish Herd Theory (W.D.Hamilton 1971 – citations > 2000)
• Reduce predation risk
• Who is protected in the center? Who is at the periphery?
• Models for simulation
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6. Algorithms & Applications
 Few Algorithms
 Ant Colony Optimization
 Particle Swarm Optimization
 Intelligent Water Drops
 The Bees
 Bat
 Termites
 Sample Applications
 Traveling Salesman
 Crown Simulation
 Ad-hoc Networks
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7. Swarm Intelligence in Termites
 After human beings, Only Termites
are the living beings who can make
standing structure 100 times bigger
than its original size.
 They build complex structure
without having blueprint or outside
intervention.
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8. Swarm Intelligence in Termites
 Not centralized. Don’t follow any
order i.e Termites have no
supervisors.
 Stigmergy
 Kind of implicit communication
i.e they observe each other’s
changes to the environment
and act accordingly).
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9. System inspired by Termites
• Inspired by termites, Harvard University Researchers have
designed a construction crew of tiny robots able to build
complicated structures (Towers, Castle & Pyramids)
without blueprints.
• They are 8 inches long and 4.5 inches wide and have
pinwheeled shaped tyres.
• “Every robot acts independently but together they will end
up building what you want.” said team leader Justin
Werfel.
• To sense its surroundings, each robot is equipped with an
infrared sensor, an ultrasound sensor & an accelerometer.
• The robots can sense the bricks they carry and the other
robots nearby.
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10. System inspired by Termites
 Each robot can walk around the
structure until it sees something
that needs to be done and then
does it.
 A human user need to only design a
structure. Software automatically
generates the rules that guide the
robots.
 They usually can recognize mistakes
they make and correct them.
 Advantage : These can be extremely
useful in situations where human
intervention is difficult, dangerous
such as building structures in space
and in disaster zones. 10

11. SI in Ad hoc networks
• Probabilistic based algorithm (PERA) for Ad hoc networks firstly
proposed in 2002 , and this gave an edge to TERMITE based
algorithm proposed in 2003[13].
• Termite based routing algorithm achieve better adaptively, lower
control overhead, and better packet delivery than contemporary
solutions.
• Each node maintains pheromone table.
Network Topology Pheromone Table at Node S
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12. Recent developments in SI
• U.S. Military is applying SI techniques to control of unmanned
vehicles.
• SI techniques are applied to load balancing in telecommunication
networks[7].
• Entertainment industry is applying SI techniques for battle and crowd
scenes. E.g., used in Lord of the Rings
• Medical Research is trying SI based controls for nanobots to fight
cancer[6].
Unmanned Vehicles Nanobot on Brain cells
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13. SI Concerns
o Convergence is guaranteed but Time to Convergence is uncertain.
o Not suitable for time critical applications E.g., nuclear reactor
temperature controller.
o Parameter Tuning.
o Most of the parameters are problem dependent. E.g.,
PID(Proportional Integral derivative) controller used in real world
control problems.[1]
o Stagnation: Premature convergence to a local optimum.
o Caused due to lack of central coordination.
o Multi-Objective optimization problems.
o E.g., Optimize f1(x) = x1 and f2(x) = x2/ax1.
o Can we design agent-level behaviours in order to obtain a certain desired
behaviour at the collective-level ?
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14. Conclusion
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In Conclusion
• Heuristics to solve difficult optimization problems.
• Has wide variety of applications.
• Basic theme of Natural Computing: Observe nature, mimic nature.

15. References
7. Ant-based Load Balancing in
Telecommunications Networks, HP
Labs Technical Reports.
8. A Probabilistic Emergent Routing
Algorithm for Mobile Ad Hoc
Networks, Baras, Mehta.
9. Geometry for the Selfish Herd, W. D.
Hamilton.
10. Swarm Smarts, Bonabeau, Theraulaz.
11. Swarm Intelligence: From Natural to
Artificial Systems, Bonabeau, Dorigo,
Theraulaz.
12. Swarm Intelligence, Introduction &
Applications, Christian Blum, Daniel
Merkle.
13. TERMITE: A Swarm Intelligent
Routing Algorithm For Mobile
Wireless Networks. M. H. Roth. Ph.D.
thesis, Cornell University 2005.
15
1. Auto Tuning of PID Controller Using
Swarm Intelligence, M. H. T. Omar, W.
M. Ali, M. Z. Mostafa.
2. Semantic Web Reasoning by Swarm
Intelligence, Kathrin Dentler,
Christophe Gu eret, and Stefan
Schlobach.
3. DCT-Based Robust Watermarking with
Swarm Intelligence Concepts, Hsiang-
Cheh Huang , Kaohsiung, Yueh-Hong
Chen, Guan-Yu Lin.
4. Robot Swarms in an Uncertain World:
Controllable Adaptability, Olga
Bogatyreva, Alexandr Shillerov.
5. xTune
6. Video on Nanobots: A cancer
treatment revolution powered by tiny
robot

16. Q & A
Thank You!
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17. 17
Annexure

18. 18
Particle Swarm Optimization
Source: Professor David Wolfe Corne's Talk on PSO
Goal: Find the deepest part of the
ocean to catch the fish.

19. 19
Particle Swarm Optimization
Source: Professor David Wolfe Corne's Talk on PSO
Step1: Place the particles.
Step2: Initialize velocities.
Step3: Particiles find the best
neighbours and move towards
them in discrete time.
Step4: Repeat Step2 (find a better
often slower velocities) and Step3.
Challenge: Parameter Tuning!

20. 20
BAT Algorithm
How? Control flying velocity and frequency of sound generation.
Why? Search for food.

21. 21
Comparison of Algorithms
Source: Comparison of algorithms, Khan, Sahai, 2012.
Conclusion:
Bat algorithm outperforms all
other algorithms for training feed
forward Neural networks!