SWARM INTELLIGENCE

1. SWARM INTELLIGENCE Kavita Singh CS-A 0509710047

2. Introduction What is Swarm Intelligence (SI)? “The emergent collective intelligence of groups of simple agents.”

3. Examples • group foraging of social insects • cooperative transportation • division of labour • nest-building of social insects • collective sorting and clustering

4. HISTORY

5. Why do we need new computing techniques? The computer revolution changed human societies: • Communication • Transportation • Industrial production • Administration, writing and bookkeeping • Technological advances • Entertainment However, some problems cannot be tackled with traditional hardware and software!

6. Drawbacks of traditional techniques Computing tasks have to be :- • Well-defined • Fairly predictable • Computable in reasonable time with serial computers.

7. Hard problems • Well-defined, but computational hard problems • NP hard problems (Travelling Salesman Problem) • Action-response planning (Chess playing)

9. What are the alternatives? • DNA based computing (chemical computation) is inspired by the human evolution. • Artificial neural network is a simplified model of human brain. • Bio-computing(simulation of biological mechanisms).

13. Working

14. Two principles of Swarm Intelligence 1. Self-Organization is based on:- • Positive feedback(amplification) • Negative feedback (for balancing) • Amplification of fluctuations(random walks, errors) • Multiple interactions 2. Stigmergy:- Indirect communication via interaction with environment.

16. Pheromone Trails • Species lay chemical substance pheromone while travelling from nest, to nest or possibly in both directions. • Pheromones evaporate. • Pheromones accumulate with multiple ants using same path.

17. The natural behavior of these ants and be programmed into an ant algorithm, which we can use to find the shortest path within graphs.

19. As ants move they leave behind a chemical substance called pheromone, which other ants can smell and identify that an ant has been there before.

23. Why is Swarm Intelligence interesting for IT?Analogies in IT and social insects • distributed system of interacting autonomous agents • goals: performance optimization and robustness • self-organized control and cooperation (decentralized) • division of labor and distributed task allocation • indirect interactions

24. How do we design Swarm Intelligence Systems? It is a 3-step process. • Identification of analogies: in swarm biology and IT systems. • Understanding: computer modeling of realistic swarm biology. • Engineering: model simplification and tuning for IT applications.

25. Solving some of the NP-Hard Problems using Swarm Intelligence

26. Problems • Complex NP complete problems. • Vehicle routing. • Network maintenance. • The traveling salesperson. • Computing the shortest route between two points.

27. Failure in solving these problems • Learning algorithms developed with artificial intelligence systems such as neural networks but imperfections and inefficiencies in both the hardware and software have prevent reliable results. • Genetic algorithms also made an attempt at these problems, and had some success. The algorithms were considered too complex to re-implement.

28. Scientists, now, are looking into the world of insects in search of new methods and approaches of attacking complex problems.

29. Travelling Salesman Problem • Visit cities in order to make sales. • Save on travel costs. • Visit each city once (Hamiltonian circuit).

30. Combinatorial Explosion in Travelling Salesman Problem • If there are N cities, then the number of different paths among them is 1.2……(N-1). • Time to examine single path = N. • Total time to perform the search = (N-1)! • For 10 cities, time reqd. = 10! = 3,268,800.

31. Solution of TSP by Swarm Intelligence • Use of agents for TSP problem. • They sense and dispense pheromone. • Memory to back step through the graph. • Each agent starts at a random starting city. • Once agent finishes a tour, it determines the size of the tour. • Then pheromone is added to the tour, the shorter the tour, the higher the pheromone level.

32. No guarantee that the first tour the agents will converge the shortest path. • Agents explore other tours. • The “stray” agent finds a shorter path. • Adjusts the pheromone levels. • Plenty of computing time needed to converge on the optimal tour. • The ant algorithm approach will still solve faster than other algorithms.

33. Vehicle routing by Swarm Optimization • Vehicle routing is similar to the TSP problem. • Employee services the client by going to them. • Minimize cost. • Use the same optimal Hamiltonian circuit as in the TSP problem.

34. Use of Swarm Intelligence in Economy • The economy is an example of SI that most researchers forget to consider. • SI demonstrates complex behavior that arises from simple individual interactions. • No one can control the economy, as there are no groups that can consistently control the economy.

35. The reaction of the population causes the the economy to slow down. • Simulating an economy using ant algorithms. • Makes it possible to control or predict the ebb and flow of this complex behavior. • Swarm intelligence, is still in its infancy. • A project such as simulating the economy is still far beyond the its capability.

36. Conclusion • Scientists are realizing SI’s potential. • The use of ant algorithms within computing systems has helped to solidify swarm intelligence’s place in the computing world. • Already researchers are observing other social animals, such as bees and schools of fish in order to utilize it in future applications and algorithms.

37. References • http://www.engr.iupui.edu/~eberhart/ • http://users.erols.com/cathyk/jimk.html • http://www.alife.org/ • http://www.aridolan.com/ • http://www.red3d.com/cwr/boids/ • http://iridia.ulb.ac.be/~mdorigo/ACO/ACO.html • http://www.engr.iupui.edu/~shi/Coference/psopap4.html