This document discusses swarm intelligence, which is inspired by natural phenomena like bird flocking and ant foraging behavior. It describes how swarms exhibit complex behavior through individuals following simple rules without centralized control. Two swarm algorithms are covered: ant colony optimization, which is applied to the traveling salesman problem, and the bee algorithm. The document compares these algorithms and discusses applications of swarm intelligence concepts.

1. Swarm IntelligenceSwarm Intelligence
05005028 (sarat chand)
05005029(naresh Kumar)
05005031(veeranjaneyulu)
05010033(kalyan raghu)

2. Swarms

Natural phenomena as inspiration

A flock of birds sweeps across the Sky.

How do ants collectively forage for food?

How does a school of fish swims, turns together?

They are so ordered.

3. 
What made them to be so ordered?

There is no centralized controller

But they exhibit complex global behavior.

Individuals follow simple rules to interact with
neighbors .

Rules followed by birds
− collision avoidance
− velocity matching
− Flock Centering

4. Swarm Intelligence-Definition

“Swarm intelligence (SI) is artificial intelligence
based on the collective behavior of decentralized,
self-organized systems”

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

6. Overview

Ant colony optimization

TSP

Bees Algorithms

Comparison between bees and ants

Conclusions

7. Ant Colony Optimization

The way ants find their food in shortest path is
interesting.

Ants secrete pheromones to remember their path.

These pheromones evaporate with time.

8. Ant Colony Optimization..

Whenever an ant finds food , it marks its return
journey with pheromones.

Pheromones evaporate faster on longer paths.

Shorter paths serve as the way to food for most of
the other ants.

9. Ant Colony Optimization

The shorter path will be reinforced by the
pheromones further.

Finally , the ants arrive at the shortest path.

10. Optimization using SI

Swarms have the ability to solve problems

Ant Colony Optimization (ACO) , a meta-heuristic

ACO can be used to solve hard problems like TSP,
Quadratic Assignment Problem(QAP)

We discuss ACO meta-heuristic for TSP

11. ACO-TSP

Given a graph with n nodes, should give the
shortest Hamiltonian cycle

m ants traverse the graph

Each ant starts at a random node

12. Transitions

Ants leave pheromone trails when they make a
transition

Trails are used in prioritizing transition

13. Transitions

Suppose ant k is at u.

Nk(u) 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(u) with
probability
puv(k) = Tuv ( 1/ d(u,v))

14. Iteration of AOC-STP

m ants are started at random nodes

They traverse the graph prioritized on trails and
edge-weights

An iteration ends when all the ants visit all nodes

After each iteration, pheromone trails are updated.

15. Updating Pheromone trails

New trail should have two components
− Old trail left after evaporation and
− Trails added by ants traversing the edge during the
iteration

T'uv = (1-p) Tuv + ChangeIn(Tuv)

Solution gets better and better as the number of
iterations increase

16. Performance of TSP with ACO heuristic

Performs better than state-of-the-art TSP
algorithms for small (50-100) of nodes

The main point to appreciate is that Swarms give
us new algorithms for optimization

17. Bee Algorithm

19. Bees Foraging

Recruitment Behaviour :
− Waggle Dancing
− series of alternating left and right loops
− Direction of dancing
− Duration of dancing

Navigation Behaviour :
− Path vector represents knowledge representation of
path by inspect
− Construction of PI.

20. Algorithm

It has two steps :
− ManageBeesActivity()
− CalculateVectors()

ManageBeesActivity: It handles agents activities
based on their internal state. That is it decides
action it has to take depending on the knowledge it
has.

CalculateVectors : It is used for administrative
purposes and calculates PI vectors for the agents.

21. Uses of Bee Algorithm

Training neural networks for pattern recognition

Forming manufacturing cells.

Scheduling jobs for a production machine.

Data clustering

22. Comparisons

Ants use pheromones for back tracking route to
food source.

Bees instead use Path Integration. Bees are able to
compute their present location from past trajectory
continuously.

So bees can return to home through direct route
instead of back tracking their original route.

Does path emerge faster in this algorithm.

23. Results

Experiments with different test cases on these
algorithms show that.
− Bees algorithm is more efficient when finding and
collecting food, that is it takes less number of steps.
− Bees algorithm is more scalable it requires less
computation time to complete task.
− Bees algorithm is less adaptive than ACO.

24. Applications of SI

In Movies : Graphics in movies like Lord of the
Rings trilogy, Troy.

Unmanned underwater vehicles(UUV):
− Groups of UUVs used as security units
− Only local maps at each UUV
− Joint detection of and attack over enemy vessels by co-
ordinating within the group of UUVs

25. More Applications

Swarmcasting:
− For fast downloads in a peer-to-peer file-sharing
network
− Fragments of a file are downloaded from different
hosts in the network, parallelly.

AntNet : a routing algorithm developed on the
framework of Ant Colony Optimization

BeeHive : another routing algorithm modelled on
the communicative behaviour of honey bees

26. A Philosophical issue

Individual agents in the group seem to have no
intelligence but the group as a whole displays
some intelligence

In terms of intelligence, whole is not equal to sum
of parts?

Where does the intelligence of the group come
from ?

Answer : Rules followed by individual agents

27. 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.

28. 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.