Swarm intelligence
- 2. OUTLINE Background What is a Swarm Intelligence (SI)? Examples from nature Origins and Inspirations of SI Ant Colony Optimization Particle Swarm Optimization Summary Why do people use SI? Advantages of SI Recent developments in SI
- 3. WHAT IS A SWARM? A loosely structured collection of interacting agents Agents: Individuals that belong to a group (but are not necessarily identical) They contribute to and benefit from the group They can recognize, communicate, and/or interact with each other
- 4. EXAMPLES OF SWARMS IN NATURE: Classic Example: Swarm of Bees Can be extended to other similar systems: Ant colony Agents: ants Flock of birds Agents: birds Traffic Agents: cars Crowd Agents: humans Immune system Agents: cells and molecules
- 5. DUMB PARTS, PROPERLY CONNECTED INTO A SWARM, YIELD SMART RESULTS. KEVIN KELLY
- 6. SWARM INTELLIGENCE Swarm intelligence is an emerging field of biologically-inspired artificial intelligence based on the behavioral models of social insects such as ants, bees, wasps, termites etc.
- 7. SWARM INTELLIGENCE (SI) An artificial intelligence (AI) technique based on the collective behavior in decentralized, self-organized systems Generally made up of agents who interact with each other and the environment No centralized control structures Based on group behavior found in nature
- 8. WITH THE RISE OF COMPUTER SIMULATION MODELS: Scientists began by modeling the simple behaviors of ants Leading to the study of how these models could be combined (and produce better results than the models of the individuals) swarm of Ants swarm of robots
- 9. WHY INSECTS? Insects have a few hundred brain cells However, organized insects have been known for: Architectural marvels Complex communication systems Resistance to hazards in nature
- 10. TWO COMMON SI ALGORITHMS Ant Colony Optimization Particle Swarm Optimization
- 11. ANT COLONY OPTIMIZATION (ACO)
- 12. ANT COLONY OPTIMIZATION (ACO) The study of artificial systems modeled after the behavior of real ant colonies and are useful in solving discrete optimization problems Introduced in 1992 by Marco Dorigo Originally called it the Ant System (AS) Has been applied to Traveling Salesman Problem (and other shortest path problems) Several NP-hard Problems
- 13. AN IN-DEPTH LOOK AT REAL ANT BEHAVIOR
- 16. THE NEW SHORTEST PATH
- 17. ADAPTING TO ENVIRONMENT CHANGES
- 18. ADAPTING TO ENVIRONMENT CHANGES
- 19. ARTIFICIAL ANTS A set of software agents Based on the pheromone model Pheromones are used by real ants to mark paths. Ants follow these paths (i.e., trail-following behaviors) Stochastic: having a random probability distribution or pattern that may be analysed statistically but may not be predicted precisely. Incrementally build solutions by moving on a graph Constraints of the problem are built into the heuristics of the ants
- 20. APPLICATIONS OF ACO Vehicle routing with time window constraints Network routing problems Assembly line balancing Data mining
- 21. TWO COMMON SI ALGORITHMS Ant Colony Optimization Particle Swarm Optimization
- 22. PARTICLE SWARM OPTIMIZATION (PSO) A population based stochastic optimization technique Searches for an optimal solution in the computable search space Developed in 1995 by Dr. Eberhart and Dr. Kennedy Inspiration: Swarms of Bees, Flocks of Birds, Schools of Fish
- 23. BASIC IDEA I Each particle is searching for the optimum Each particle is moving and hence has a velocity. Each particle remembers the position it was in where it had its best result so far (its personal best) But this would not be much good on its own; particles need help in figuring out where to search.
- 24. THE BASIC IDEA II The particles in the swarm co-operate. They exchange information about what they’ve discovered in the places they have visited The co-operation is very simple. In basic PSO it is like this: A particle has a neighbourhood associated with it. A particle knows the fitnesses of those in its neighbourhood, and uses the position of the one with best fitness. This position is simply used to adjust the particle’s velocity
- 25. MORE ON PSO In PSO individuals strive to improve themselves and often achieve this by observing and imitating their neighbors Each PSO individual has the ability to remember PSO has simple algorithms and low overhead Making it more popular in some circumstances than Genetic/Evolutionary Algorithms Has only one operation calculation: Velocity: a vector of numbers that are added to the position coordinates to move an individual
- 26. APPLICATIONS OF PSO Human tremor analysis Human performance assessment Ingredient mix optimization Evolving neural networks to solve problems
- 27. BEHAVIOURAL ANIMATION: • The particle swarm technology concepts are being applied in computer graphics area and can be found in Batman Returns (1992), The Lion King (1994) and From Dusk Till Dawn (1996). • The most impressive usage are probably the immense battle sequences in the trilogy Lord of the Rings where about 250,000 individual fighters.
- 28. SWARM ROBOTICS Swarm Robotics The application of SI principles to robotics A group of simple robots that can only communicate locally and operate in a biologically inspired manner A currently developing area of research
- 29. WHY DO PEOPLE USE ACO AND PSO? Can be applied to a wide range of applications Easy to understand Easy to implement Computationally efficient
- 30. ADVANTAGES OF SI The systems are scalable The systems are flexible The systems are robust The systems are able to adapt to new situations easily
- 31. DISADVANTAGES OF SI Non-optimal – Because swarm systems are highly redundant and have no central control, they tend to be inefficient. The allocation of resources is not efficient, and duplication of effort is always rampant. Uncontrollable – It is very difficult to exercise control over a swarm.
- 32. RECENT DEVELOPMENTS IN SI APPLICATIONS U.S. Military is applying SI techniques to control of unmanned vehicles NASA is applying SI techniques for planetary mapping Medical Research is trying SI based controls for nanobots to fight cancer SI techniques are applied to load balancing in telecommunication networks Entertainment industry is applying SI techniques for battle and crowd scenes
- 33. CLOSING ARGUMENTS Still very theoretical No clear boundaries Details about inner workings of insect swarms The future…???
- 34. Satellite Maintenance THE FUTURE? Medical Interacting Chips in Mundane Objects Cleaning Ship HullsPipe Inspection Pest Eradication Miniaturization Engine Maintenance Telecommunications Self-Assembling Robots Job Scheduling Vehicle Routing Data Clustering Distributed Mail Systems Optimal Resource Allocation Combinatorial Optimization
- 35. THANK YOU