Swarm   intelligence<br />“The emergent collective intelligence of groups of simple agents.”<br />
What?<br />Collective behavior of decentralized, self-organizing systems<br />Natural or Artificial<br />Simple local beha...
Why is it of Interest?<br />Distributed system of interacting autonomous agents<br />Goals: performance optimization and r...
Nature<br />Swarm Intelligence<br />4<br />
Ant Foraging Behavior<br />Shortest path between food and nest<br />Indirect Communication – Pheromones<br />Ant productiv...
Ant Colony Optimization (ACO)<br />Probabilistic Technique for Solving Computational Problems<br />Graph Problems<br />Dep...
Application of ACO<br />Scheduling Problem<br />Vehicle Routing Problem<br />Assignment Problem<br />Set Problem<br />Swar...
Particle Swarm Optimization<br />ORIGINS:<br />How can birds or fish exhibit such a coordinated and collective behavior?<b...
Particle Swarm Optimization<br />A computational method that optimizes a problem by iteratively trying to improve a candid...
Particle Swarm Optimization<br />For each particle     Initialize particleENDDo    For each particle         Calculate fit...
Particle Swarm Optimization<br />Applications:<br />Function Optimisation<br />Optimal Control in Control Systems<br />Swa...
Other Algorithms<br />Cuckoo Search<br />Intelligent Water Drops<br />River Formation Dynamics<br />Firefly Algorithm<br /...
Application of Swarm Intelligence<br />Crowd Simulation<br />Movies<br />Lord of the Rings (Massive technology)<br />Batma...
Pop Culture<br />Prey – Michael Crichton<br />Allucination – Isaac Asimov<br />Matrix (movie)<br />Mass Effect (video game...
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Swarm Intelligence - An Introduction

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An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.

This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.

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Swarm Intelligence - An Introduction

  1. 1. Swarm intelligence<br />“The emergent collective intelligence of groups of simple agents.”<br />
  2. 2. What?<br />Collective behavior of decentralized, self-organizing systems<br />Natural or Artificial<br />Simple local behavior leads to global intelligent behavior<br />Ant colonies, bird flocking, bacterial growth<br />2<br />Swarm Intelligence<br />
  3. 3. Why is it of Interest?<br />Distributed system of interacting autonomous agents<br />Goals: performance optimization and robustness<br />Division of labor and distributed task allocation<br />Self-organized control and cooperation (decentralized)<br />Swarm Intelligence<br />3<br />
  4. 4. Nature<br />Swarm Intelligence<br />4<br />
  5. 5. Ant Foraging Behavior<br />Shortest path between food and nest<br />Indirect Communication – Pheromones<br />Ant productivity is better than the sum of their single activities<br />Cooperation and Division of Labor<br />5<br />Swarm Intelligence<br />
  6. 6. Ant Colony Optimization (ACO)<br />Probabilistic Technique for Solving Computational Problems<br />Graph Problems<br />Depends on two factors<br />Attractiveness of the move from node i to j<br />Trail level of the move – proficiency of the move in the past<br />Swarm Intelligence<br />6<br />
  7. 7. Application of ACO<br />Scheduling Problem<br />Vehicle Routing Problem<br />Assignment Problem<br />Set Problem<br />Swarm Intelligence<br />7<br />
  8. 8. Particle Swarm Optimization<br />ORIGINS:<br />How can birds or fish exhibit such a coordinated and collective behavior?<br />The study of the above mentioned problem accidentally revealed that PSO is an optimization technique.<br />Swarm Intelligence<br />8<br />
  9. 9. Particle Swarm Optimization<br />A computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.<br />Particle(candidate solution) improves its position based on 3 criteria:<br />Inertia<br />Personal Influence<br />Social Influence<br />Swarm Intelligence<br />9<br />
  10. 10. Particle Swarm Optimization<br />For each particle     Initialize particleENDDo    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 to velocity equation.        Update particle position according to position equation.    End While maximum iterations or minimum error criteria is not attained<br />Swarm Intelligence<br />10<br />
  11. 11. Particle Swarm Optimization<br />Applications:<br />Function Optimisation<br />Optimal Control in Control Systems<br />Swarm Intelligence<br />11<br />
  12. 12. Other Algorithms<br />Cuckoo Search<br />Intelligent Water Drops<br />River Formation Dynamics<br />Firefly Algorithm<br />12<br />Swarm Intelligence<br />
  13. 13. Application of Swarm Intelligence<br />Crowd Simulation<br />Movies<br />Lord of the Rings (Massive technology)<br />Batman Returns (Simulation of bats)<br />Airlines – Passenger Simulation<br />Telecom Network<br />Airport Gates<br />Process Optimization<br />13<br />Swarm Intelligence<br />
  14. 14. Pop Culture<br />Prey – Michael Crichton<br />Allucination – Isaac Asimov<br />Matrix (movie)<br />Mass Effect (video game)<br />14<br />Swarm Intelligence<br />

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