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Swarm Intelligence

Swarm Intelligence

05005028 (sarat chand)05005029(naresh Kumar)05005031(veeranjaneyulu)05010033(kalyan raghu)

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.

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

Swarm Intelligence-Definition

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

Characteristics of Swarms

- Composed of many individuals
- Individuals are homogeneous
- Local interaction based on simple rules
- Self-organization

Overview

- Ant colony optimization
- TSP
- Bees Algorithms
- Comparison between bees and ants
- Conclusions

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.

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.

Ant Colony Optimization

- The shorter path will be reinforced by the pheromones further.
- Finally , the ants arrive at the shortest path.

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

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

Transitions

- Ants leave pheromone trails when they make a transition
- Trails are used in prioritizing transition

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))

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.

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

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

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.

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.

Uses of Bee Algorithm

- Training neural networks for pattern recognition
- Forming manufacturing cells.
- Scheduling jobs for a production machine.
- Data clustering

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.

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.

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

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

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

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.

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.

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