Swarm Intelligence State of the Art

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Swarm Intelligence State of the Art

  1. 1. Swarm Intelligence State of the Art <ul><ul><li>Marek Kopel @PWr </li></ul></ul><ul><ul><li>ACO, PSO, SDS, </li></ul></ul><ul><ul><li>Stigmetry </li></ul></ul>
  2. 2. SI - Swarm intelligence <ul><li>AI based on the collective behavior of decentralized, self-organized systems </li></ul><ul><li>by Gerardo Beni and Jing Wang in the context of cellular robotic systems, 1989 </li></ul><ul><li>a population of simple agents interacting locally with one another and with their environment </li></ul><ul><li>agents follow simple rules - no centralized control structure </li></ul><ul><li>emergence of complex global behavior </li></ul><ul><li>Natural examples of SI </li></ul><ul><ul><ul><li>ant colonies </li></ul></ul></ul><ul><ul><ul><li>bird flocking </li></ul></ul></ul><ul><ul><ul><li>animal herding </li></ul></ul></ul><ul><ul><ul><li>bacterial growth </li></ul></ul></ul><ul><ul><ul><li>fish schooling </li></ul></ul></ul>from Wikipedia, the free encyclopedia In The Matrix movies, the robotic sentinels exhibit signs of SI.
  3. 3. Stigmetry <ul><li>a form of self-organization </li></ul><ul><li>mechanism of spontaneous, indirect coordination between agents or actions </li></ul><ul><li>the trace left in the environment by an action stimulates the performance of a subsequent action (by the same or a different agent) </li></ul><ul><li>produces complex, apparently intelligent structures , without need for any planning, control, or even communication </li></ul><ul><li>efficient collaboration between extremely simple agents, who lack any memory, intelligence or even awareness of each other </li></ul>from Wikipedia, the free encyclopedia
  4. 4. Stigmetry (2) <ul><li>first observed in social insects ( eusocial ) </li></ul><ul><li>example: ants exchange information by laying down pheromones on their way back to the nest when they have found food. In that way, they collectively develop a complex network of trails , connecting the nest in the most efficient way to the different food sources. </li></ul><ul><li>On Inet: users interact only by modifying local parts of their shared virtual environment. </li></ul><ul><li>Wikipedia - perfect example : the massive structure of information available in a wiki - termite nest ; one initial user leaves a seed of an idea (a mudball) which attracts other users who then build upon and modify this initial concept eventually c onstructing an elaborate structure of connected thoughts </li></ul>from Wikipedia, the free encyclopedia
  5. 5. SI example 1 <ul><li>Ant colony optimization </li></ul><ul><ul><ul><li>a class of optimization algorithms </li></ul></ul></ul><ul><ul><ul><li>modeled on the actions of an ant colony </li></ul></ul></ul><ul><ul><ul><li>Artificial 'ants' - simulation agents </li></ul></ul></ul><ul><ul><ul><li>locate optimal solutions by moving through a parameter space representing all possible solutions </li></ul></ul></ul><ul><ul><ul><li>real ants lay down pheromones directing each other to resources </li></ul></ul></ul><ul><ul><ul><li>simulated 'ants' similarly record their positions and the quality of their solutions </li></ul></ul></ul><ul><ul><ul><li>in later simulation iterations more ants locate better solutions </li></ul></ul></ul><ul><ul><ul><li>a variation : bees algorithm , which is more analogous to the foraging patterns of the honey bee </li></ul></ul></ul>from Wikipedia, the free encyclopedia
  6. 6. ACO in action <ul><li>ACO simulators: </li></ul><ul><ul><li>http://www.rennard.org/alife/english/antsgb.html (Java applet) </li></ul></ul><ul><ul><li>http://djoh.net/blog/ANTColony/applet.html (Java applet) </li></ul></ul><ul><ul><li>http://www.geocities.com/chamonate/hormigas/antfarm/ (.zip) </li></ul></ul><ul><ul><li>http://www.nightlab.ch/antsim/ (.rar) </li></ul></ul>
  7. 7. SI example 2 <ul><li>Particle swarm optimization </li></ul><ul><ul><ul><li>a global optimization algorithm </li></ul></ul></ul><ul><ul><ul><li>problems in which a best solution can be represented as a point or surface in an n-dimensional space </li></ul></ul></ul><ul><ul><ul><li>hypotheses are plotted and seeded with an initial velocity </li></ul></ul></ul><ul><ul><ul><li>a communication channel between the particles </li></ul></ul></ul><ul><ul><ul><li>particles @ each timestep: </li></ul></ul></ul><ul><ul><ul><ul><li>move through the solution space </li></ul></ul></ul></ul><ul><ul><ul><ul><li>are evaluated according to some fitness criterion </li></ul></ul></ul></ul><ul><ul><ul><li>over time, particles are accelerated towards those particles ( within their communication grouping ) which have better fitness values </li></ul></ul></ul>from Wikipedia, the free encyclopedia
  8. 8. SI example 3 <ul><li>Stochastic diffusion search </li></ul><ul><ul><ul><li>agent based on probabilistic global search and optimization technique </li></ul></ul></ul><ul><ul><ul><li>problems: objective function can be decomposed into multiple independent partial-functions </li></ul></ul></ul><ul><ul><ul><li>each agent maintains a hypothesis - iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis </li></ul></ul></ul><ul><ul><ul><li>standard version: partial function evaluations are binary resulting in each agent becoming active or inactive </li></ul></ul></ul><ul><ul><ul><li>information on hypotheses is diffused across the population via inter-agent communication </li></ul></ul></ul><ul><ul><ul><li>unlike the stigmergic comm. used in ACO, in SDS agents communicate via a one-to-one comm. strategy analogous to the tandem running procedure observed in some species of ant </li></ul></ul></ul><ul><ul><ul><li>positive feedback mechanism ensures that, over time, a population of agents stabilise around the global-best solution </li></ul></ul></ul>from Wikipedia, the free encyclopedia
  9. 9. ACO - Ant Colony Optimization <ul><li>metaheuristic (heuristic method for solving a very general class of computational problems by combining user-given black-box procedures - usually heuristics themselves) </li></ul><ul><li>by Marco Dorigo , PhD thesis,1992 </li></ul><ul><li>technique for solving computational problems </li></ul><ul><li>which can be reduced to finding good paths through graphs </li></ul><ul><li>inspired by the behaviour of ants </li></ul><ul><li>in finding paths from the colony to food </li></ul>from Wikipedia, the free encyclopedia
  10. 10. ACO related methods <ul><li>Genetic Algorithms (GA) </li></ul><ul><ul><ul><li>maintain a pool of solutions rather than just one. The process of finding superior solutions mimics that of evolution, with solutions being combined or mutated to alter the pool of solutions, with solutions of inferior quality being discarded. </li></ul></ul></ul><ul><li>Simulated Annealing (SA) </li></ul><ul><ul><ul><li>a related global optimization technique which traverses the search space by generating neighbouring solutions of the current solution. A superior neighbour is always accepted. An inferior neighbour is accepted probabilistically based on the difference in quality and a temperature parameter. The temperature parameter is modified as the algorithm progresses to alter the nature of the search. </li></ul></ul></ul><ul><li>Tabu search (TS) </li></ul><ul><ul><ul><li>similar to Simulated Annealing, in that both traverse the solution space by testing mutations of an individual solution. While simulated annealing generates only one mutated solution, tabu search generates many mutated solutions and moves to the solution with the lowest fitness of those generated. In order to prevent cycling and encourage greater movement through the solution space, a tabu list is maintained of partial or complete solutions. It is forbidden to move to a solution that contains elements of the tabu list, which is updated as the solution traverses the solution space. </li></ul></ul></ul><ul><li>Harmony search (HS) </li></ul><ul><ul><ul><li>algorithm based on the analogy between music improvisation and optimization. Each musician (variable) together seeks better harmonies (vectors). </li></ul></ul></ul>from Wikipedia, the free encyclopedia
  11. 11. Vitorino Ramos coming soon to a university near You <ul><li>CEC 07 - IEEE Press - Vitorino Ramos Computational Chemotaxis 70. Ramos, V., Fernandes, C., Rosa, A.C., Abraham, A., Computational Chemotaxis in Ants and Bacteria over Dynamic Environments, in CEC´07 - Congress on Evolutionary Computation, IEEE Press, USA, ISBN 1-4244-1340-0, pp. 1009-1017, Sep. 2007. </li></ul><ul><li>GECCO 07 - ACM Press - Vitorino Ramos, Binary ant Algorithm, Vol. 1 pp. 41-48 69. Fernandes, C., Rosa, A.C., Ramos V., Binary Ant Algorithm, in Dirk Thierens et al. (Eds.), GECCO´07 - Genetic and Evolutionary Computation Conference, Vol. 1, pp. 41-48, ACM Press, London, UK, 7-11 July, 2007. </li></ul><ul><li>68. Vitorino Ramos, Self-Organized Co-Evolutionary Complex Network Learning on Bounded Rationality Environments, submitted to Adaptive Behaviour Journal, 2007. </li></ul><ul><li>64. Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Societal Implicit Memory and his Speed on Tracking Extrema over Dynamic Environments using Self-Regulatory Swarms , to appear soon. </li></ul><ul><li>63. Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes, submitted to A. Porto, A. Pazos, W. Buno (Eds.), Advancing Artificial Intelligence through Biological Process Applications, IDEA Group Inc., 2007. </li></ul><ul><li>Vitorino Ramos - Swarm Intelligence in Data Mining - Springer 62. Abraham, Ajith; Grosan, Crina; Ramos, Vitorino (Eds.), Swarm Intelligence in Data Mining, Studies in Computational Intelligence (series), Vol. 34, Springer-Verlag, ISBN: 3-540-34955-3, Approx. 270 p., Hardcover, 2006. </li></ul><ul><li>http://www.chemoton.org/books.html </li></ul>

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