1. -Abhilash Nayak Regd. No. :0801227285 CS1(B) abhilash.nayak01@gmail.com “The Power of Simplicity”

2. What is Swarm Intelligence (SI)? “The emergent collective intelligence of groups of simple agents.” Swarm intelligence (SI) as defined by Bonabeau, Dorigo and Theraulaz is “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies”

3. Swarms build colonies and work in a coordinated manner — yet no single member of the swarm is in control. Flocks of birds coordinate to move without collision. Ants manage to find food sources quickly and efficiently. Termites build giant structures. Schools of fish fend off predators and move as one body Examples

4. 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! Why do we need new computing techniques?

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

6. What are the alternatives? DNA based computing (chemical computation) Quantum computing (quantum-physical computation) Bio-computing (simulation of biological mechanisms)

7. Working

8. self-organization is based on: ◦ activity amplification by positive feedback ◦ activity balancing by negative feedback ◦ amplification of random fluctuations ◦ multiple interactions stigmergy - stimulation by work - is based on: ◦ work as behavioural response to the environmental state ◦ an environment that serves as a work state memory ◦ work that does not depend on specific agents Two principles of Swarm Intelligence

9. Why Social Colony is a source of inspiration? Flexible: the colony can respond to internal perturbations and external challenges Robust: tasks are completed even if some individuals fail Decentralized: there is no central control(ler) in the colony Self-organized: paths to solutions are emergent rather than predefined

10. Ants Why are ants interesting? ◦ ants solve complex tasks by simple local means ◦ ant productivity is better than the sum of their single activities ◦ ants are ‘grand masters’ in search and exploitation Which mechanisms are important? ◦ cooperation and division of labour ◦ adaptive task allocation ◦ work stimulation by cultivation ◦ pheromones

11. 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. Pheromone Trails

12. Ant Colony Optimization (ACO) Is inspired by the behavior of ant colonies. Ability of Optimization in finding shortest path. Ants leave a chemical pheromone trail. Pheromone trails enables them to find shortest paths between their nest and food sources Ants find the shorter path in an experimental setup

13. A bridge leads from a nest to a foraging area, (a) 4 minutes after bridge placement, (b) 8 minutes after bridge placement

14. Ant Foraging Cooperative search by pheromone trails 1.The natural behavior of these ants and be programmed into an ant algorithm, which we can use to find the shortest path within graphs. 2.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.

15. ACO algorithm Main steps of the ACO algorithm are given below: Pheromone trail initialization Solution construction using pheromone trail Each ant constructs a complete solution to the problem according to a probabilistic State transition rule. The state transition rule depends mainly on the state of the pheromone. Pheromone trail update.

16. 1 : repeat 2 : if antCount < maxAnts then 3 : create a new ant 4 : set initial state 5 : end if 6 : for all ants do 7 : determine all feasible neighbor states {considering the ant's visited states} 8 : if solution found V no feasible neighbor state then 9 : kill ant 10: if we use delayed pheromone update then 11: evaluate solution 12: deposit pheromone on all used edges 13: end if 14: else algorithm

17. 15: stochastically select a feasible neighbor state {directed by the ants memory, the pheromone concentration on the edges and local heuristics} 16: if we use step-by-step pheromone update then 17: deposit pheromone on the used edge 18: end if 19: end if 20: end for 21: evaporate pheromone until termination criterion satisfied {e.g., found a satisfying solution}

18. Applications of Ant Colony Optimization Traffic on telecommunications systems, the internet, roads, rail, and sea would all benefit from the reduction in congestion that efficient routing algorithms could provide. Modern airlines are actually putting the ant colony research to work, with impressive payback Telecommunication System Airlines

19. Particle Swarm Optimization (PSO) Idea: Used to optimize continuous functions Function is evaluated at each time step for the agent’s current position

20. Each agent “remembers” personal best value of the function (pbest) Globally best personal value is known (gbest) Both points are attracting the agent

21. Formula for one agent in one dimension: v x = v x + 2.rand().(pbest x - present x )+ 2.rand().(gbest x - present x ) ----(a) Where 0 ≤ rand() ≤ 1 present x = present x +v x ----(b)

22. The pseudo code of the procedure is as follows For each particle Initialize particle END Do For each particle Calculate fitness value If the fitness value is better than the best fitness value (pBest) in history set current value as the new pBest End Choose the particle with the best fitness value of all the particles as the gBest For each particle Calculate particle velocity according equation (a) Update particle position according equation (b) End While maximum iterations or minimum error criteria is not attained

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

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

25. Hard Problems Fuzzy problems intelligent human-machine interaction natural language understanding

26. Hard Problems Hardly predictable and dynamic problems real-world autonomous robots management and business planning

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

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

29. 1. Visit cities in order to make sales. 2. Save on travel costs. 3. Visit each city once (Hamiltonian circuit). Travelling Salesman Problem

30. 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. Combinatorial Explosion in Travelling Salesman Problem

31.  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. Solution of TSP by Swarm Intelligence

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 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. Vehicle routing by Swarm Optimization

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. 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 Why is Swarm Intelligence interesting for IT? Analogies in IT and social insects

37. 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. Failure in solving these problems

38. It is a 3-step process. 1. Identification of analogies: in swarm biology and IT systems. 2. Understanding: computer modeling of realistic swarm biology. 3. Engineering: model simplification and tuning for IT applications. How do we design Swarm Intelligence Systems?

39. Bad News, Good News Bad news Difficult to predict collective behaviour from individual rules. Interrogate one of the participants, it won’t tell you anything about the function of the group. Small changes in rules lead to different group-level behaviour. Individual behaviour looks like noise: how do you detect threats? Good news Possible to efficiently control organization or manipulate groups using simple rules. Possible to predict group-level outcome using bottom simulation.

40. Applications of SI Swarm/crowd simulation programming Computer Networks: Adaptive Routing Robotics/Artificial Intelligence Process optimization /Staff Scheduling

41. 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. Conclusion

42. Any Questions?