Swarm intelligence refers to the collective behavior that emerges from decentralized, self-organized systems, both natural and artificial. In nature, it can be seen in the ability of ant colonies and bird flocks to coordinate and complete tasks through simple local interactions between individuals. Artificial swarm intelligence systems are distributed systems of interacting autonomous agents that coordinate through self-organization to solve problems through cooperation and division of labor. Examples of algorithms inspired by swarm intelligence include ant colony optimization and particle swarm optimization.

1. Swarm intelligence “The emergent collective intelligence of groups of simple agents.”

2. What? Collective behavior of decentralized, self-organizing systems Natural or Artificial Simple local behavior leads to global intelligent behavior Ant colonies, bird flocking, bacterial growth 2 Swarm Intelligence

3. Why is it of Interest? Distributed system of interacting autonomous agents Goals: performance optimization and robustness Division of labor and distributed task allocation Self-organized control and cooperation (decentralized) Swarm Intelligence 3

4. Nature Swarm Intelligence 4

5. Ant Foraging Behavior Shortest path between food and nest Indirect Communication – Pheromones Ant productivity is better than the sum of their single activities Cooperation and Division of Labor 5 Swarm Intelligence

6. Ant Colony Optimization (ACO) Probabilistic Technique for Solving Computational Problems Graph Problems Depends on two factors Attractiveness of the move from node i to j Trail level of the move – proficiency of the move in the past Swarm Intelligence 6

7. Application of ACO Scheduling Problem Vehicle Routing Problem Assignment Problem Set Problem Swarm Intelligence 7

8. Particle Swarm Optimization ORIGINS: How can birds or fish exhibit such a coordinated and collective behavior? The study of the above mentioned problem accidentally revealed that PSO is an optimization technique. Swarm Intelligence 8

9. Particle Swarm Optimization A computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Particle(candidate solution) improves its position based on 3 criteria: Inertia Personal Influence Social Influence Swarm Intelligence 9

10. Particle Swarm Optimization 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 Swarm Intelligence 10

11. Particle Swarm Optimization Applications: Function Optimisation Optimal Control in Control Systems Swarm Intelligence 11

12. Other Algorithms Cuckoo Search Intelligent Water Drops River Formation Dynamics Firefly Algorithm 12 Swarm Intelligence

13. Application of Swarm Intelligence Crowd Simulation Movies Lord of the Rings (Massive technology) Batman Returns (Simulation of bats) Airlines – Passenger Simulation Telecom Network Airport Gates Process Optimization 13 Swarm Intelligence

14. Pop Culture Prey – Michael Crichton Allucination – Isaac Asimov Matrix (movie) Mass Effect (video game) 14 Swarm Intelligence