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 systemsNatural or ArtificialSimple local behavior leads to global intelligent behaviorAnt colonies, bird flocking, bacterial growth2Swarm Intelligence

3. Why is it of Interest?Distributed system of interacting autonomous agentsGoals: performance optimization and robustnessDivision of labor and distributed task allocationSelf-organized control and cooperation (decentralized)Swarm Intelligence3

4. NatureSwarm Intelligence4

5. Ant Foraging BehaviorShortest path between food and nestIndirect Communication – PheromonesAnt productivity is better than the sum of their single activitiesCooperation and Division of Labor5Swarm Intelligence

6. Ant Colony Optimization (ACO)Probabilistic Technique for Solving Computational ProblemsGraph ProblemsDepends on two factorsAttractiveness of the move from node i to jTrail level of the move – proficiency of the move in the pastSwarm Intelligence6

7. Application of ACOScheduling ProblemVehicle Routing ProblemAssignment ProblemSet ProblemSwarm Intelligence7

8. Particle Swarm OptimizationORIGINS: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 Intelligence8

9. Particle Swarm OptimizationA 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:InertiaPersonal InfluenceSocial InfluenceSwarm Intelligence9

10. Particle Swarm OptimizationFor 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 attainedSwarm Intelligence10

11. Particle Swarm OptimizationApplications:Function OptimisationOptimal Control in Control SystemsSwarm Intelligence11

12. Other AlgorithmsCuckoo SearchIntelligent Water DropsRiver Formation DynamicsFirefly Algorithm12Swarm Intelligence

13. Application of Swarm IntelligenceCrowd SimulationMoviesLord of the Rings (Massive technology)Batman Returns (Simulation of bats)Airlines – Passenger SimulationTelecom NetworkAirport GatesProcess Optimization13Swarm Intelligence

14. Pop CulturePrey – Michael CrichtonAllucination – Isaac AsimovMatrix (movie)Mass Effect (video game)14Swarm Intelligence