A presentation on swarm intelligence and two algorithms, Ant colony optimization and Particle swarm optimization and it's applications.

1. SWARM INTELLIGENCE
By:
Akshay Agarwal

2. OUTLINE
 Background
 What is a Swarm Intelligence (SI)?
 Examples from nature
 Origins and Inspirations of SI
 Ant Colony Optimization
 Particle Swarm Optimization
 Summary
 Why do people use SI?
 Advantages of SI
 Recent developments in SI

3. WHAT IS A 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

4. EXAMPLES OF SWARMS IN NATURE:
 Classic Example: Swarm of Bees
 Can be extended to other similar systems:
 Ant colony
 Agents: ants
 Flock of birds
 Agents: birds
 Traffic
 Agents: cars
 Crowd
 Agents: humans
 Immune system
 Agents: cells and molecules

5. DUMB PARTS, PROPERLY
CONNECTED INTO A SWARM,
YIELD SMART RESULTS.
KEVIN KELLY

6. SWARM INTELLIGENCE
 Swarm intelligence is an emerging field of
biologically-inspired artificial intelligence based on
the behavioral models of social insects such as
ants, bees, wasps, termites etc.

7. SWARM INTELLIGENCE (SI)
 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

8. WITH THE RISE OF COMPUTER SIMULATION
MODELS:
 Scientists began by
modeling the simple
behaviors of ants
 Leading to the study of how
these models could be
combined (and produce
better results than the
models of the individuals)
swarm of Ants
swarm of robots

9. WHY INSECTS?
 Insects have a few hundred brain cells
 However, organized insects have been known for:
 Architectural marvels
 Complex communication
systems
 Resistance to hazards in
nature

10. TWO COMMON SI ALGORITHMS
Ant Colony Optimization
Particle Swarm Optimization

11. ANT COLONY OPTIMIZATION (ACO)

12. ANT COLONY OPTIMIZATION (ACO)
 The study of artificial systems modeled after the
behavior of real ant colonies and are useful in
solving discrete optimization problems
 Introduced in 1992 by Marco Dorigo
 Originally called it the Ant System (AS)
 Has been applied to
 Traveling Salesman Problem (and other shortest path
problems)
 Several NP-hard Problems

13. AN IN-DEPTH LOOK AT REAL ANT BEHAVIOR

14. INTERRUPT THE FLOW

15. THE PATH THICKENS!

16. THE NEW SHORTEST PATH

17. ADAPTING TO ENVIRONMENT CHANGES

18. ADAPTING TO ENVIRONMENT CHANGES

19. ARTIFICIAL ANTS
 A set of software agents
 Based on the pheromone model
 Pheromones are used by real ants to mark paths. Ants
follow these paths (i.e., trail-following behaviors)
 Stochastic: having a random probability distribution or
pattern that may be analysed statistically but may not be
predicted precisely.
 Incrementally build solutions by moving on a graph
 Constraints of the problem are built into the
heuristics of the ants

20. APPLICATIONS OF ACO
 Vehicle routing with time window constraints
 Network routing problems
 Assembly line balancing
 Data mining

21. TWO COMMON SI ALGORITHMS
Ant Colony Optimization
Particle Swarm Optimization

22. PARTICLE SWARM OPTIMIZATION (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
 Inspiration: Swarms of Bees, Flocks of Birds,
Schools of Fish

23. BASIC IDEA I
 Each particle is searching for the optimum
 Each particle is moving and hence has a velocity.
 Each particle remembers the position it was in
where it had its best result so far (its personal best)
 But this would not be much good on its own;
particles need help in figuring out where to search.

24. THE BASIC IDEA II
 The particles in the swarm co-operate. They
exchange information about what they’ve
discovered in the places they have visited
 The co-operation is very simple. In basic PSO it is
like this:
 A particle has a neighbourhood associated with it.
 A particle knows the fitnesses of those in its
neighbourhood, and uses the position of the one with best
fitness.
 This position is simply used to adjust the particle’s velocity

25. MORE ON 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
 PSO has simple algorithms and low overhead
 Making it more popular in some circumstances than
Genetic/Evolutionary Algorithms
 Has only one operation calculation:
 Velocity: a vector of numbers that are added to the position
coordinates to move an individual

26. APPLICATIONS OF PSO
 Human tremor analysis
 Human performance assessment
 Ingredient mix optimization
 Evolving neural networks to solve problems

27. BEHAVIOURAL ANIMATION:
• The particle swarm technology concepts are being
applied in computer graphics area and can be
found in Batman Returns (1992), The Lion King
(1994) and From Dusk Till Dawn (1996).
• The most impressive usage are probably the
immense battle sequences in the trilogy Lord of the
Rings where about 250,000 individual fighters.

28. SWARM ROBOTICS
 Swarm Robotics
 The application of SI principles to robotics
 A group of simple robots that can only communicate
locally and operate in a biologically inspired manner
 A currently developing area of research

29. WHY DO PEOPLE USE ACO AND PSO?
 Can be applied to a wide range of applications
 Easy to understand
 Easy to implement
 Computationally efficient

30. ADVANTAGES OF SI
 The systems are scalable
 The systems are flexible
 The systems are robust
 The systems are able to adapt to new situations
easily

31. DISADVANTAGES OF SI
 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.

32. RECENT DEVELOPMENTS IN SI APPLICATIONS
 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
 SI techniques are applied to load balancing in
telecommunication networks
 Entertainment industry is applying SI techniques for
battle and crowd scenes

33. CLOSING ARGUMENTS
 Still very theoretical
 No clear boundaries
 Details about inner workings of insect swarms
 The future…???

34. Satellite
Maintenance
THE FUTURE?
Medical
Interacting Chips in
Mundane Objects
Cleaning Ship
HullsPipe Inspection
Pest Eradication
Miniaturization
Engine
Maintenance
Telecommunications
Self-Assembling Robots
Job Scheduling
Vehicle Routing
Data Clustering
Distributed Mail
Systems
Optimal Resource
Allocation
Combinatorial
Optimization

35. THANK YOU