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Swarm Intelligence 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. Optimizations of 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-TSP 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 Bee Algorithm 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 coordinating 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 SystemLemmens ,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 StutzleHandbook of metaheuristics, 2002.