Swarm IntelligenceBugsBusters Research Team

Table of contents

• What is meant by Swarm Intelligence?• Examples in insects life• PSO and ACO Algorithms• Applications and Recent Developments• Advantages and Disadvantages• Conclusion• References

What is meant by Swarm Intelligence?• Definition

• any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” [Bonabeau, Dorigo, Theraulaz: Swarm Intelligence] One worker of robot designed as a

worker of ant

swarm of robots swarm of Ants

Swarm of birds

Swarm of Flying robots cooperating together

What is meant by Swarm Intelligence?• It is 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 natureAgents

What is meant by Swarm Intelligence?• Insects have a few hundred brain cells

• However, organized insects have been known for:• Architectural marvels• Complex communication systems• Resistance to hazards in nature

• In the 1950’s E.O. Wilson observed:• A single ant acts (almost) randomly – often leading to its own demise

• A colony of ants provides food and protection for the entire population

Medium Real Ant nests, Taken from the earth

• This huge Ant colony Concrete, that has been Excavated from earth in several weeks.

• This Colony has roads with shortest path between every two points.

What is meant by Swarm Intelligence?• Characteristics

• Composed of many individuals

• Individuals are homogeneous

• Local interaction based on simple rules

• Self-organization

What is meant by Swarm Intelligence?• Four Ingredients of Self Organization

• Positive Feedback

• Negative Feedback

• Amplification of Fluctuations –randomness

• Reliance on multiple interactions

Example• Original Example: Swarm of Bees• Ant colony

• Agents: ants• Flock of birds

• Agents: birds• Traffic

• Agents: cars• Crowd

• Agents: humans• Immune system

• Agents: cells and molecules

Cont. Example• Ant Colony

• Every single insect in a social insect colony seems to have its own agenda, and yet an insect colony looks so organized.

• The seamless integration of all individual activities does not seem to require any supervisor.

• For Example there is in one colony different type of workers:• Leafcutter Ants• Weaver Ants• Army Ants

Cont. Examples• Leafcutter Ants

• cut leaves from plants and trees

• Workers forage for leaves hundreds of meters away from the nest,

• literally organizing highways to and from their foraging sites

Cont. Examples• Weaver Ants

• workers form chains of their own bodies, allowing them to cross wide gaps and pull stiff leaf edges together to form a nest

• Several chains can join to form a bigger one over which workers run back and forth.

• Such chains create enough force to pull leaf edges together.

Cont. Example• Army Ants

• organize impressive hunting raids, involving up to 200,000 workers, during which they collect thousands of prey

Cont. Examples• Ant Colony Swarm benefits:• Ants forage better.

• Settle in organized home.

• Defend it self against predators

• Social Insects have survived for millions of years.

Cont. Examples, How to Interact?• Direct Interactions

• Food/liquid exchange, visual contact, chemical contact (pheromones)

• Indirect Interactions (Stigmergy)• Individual behavior modifies the environment, which in turn modifies the behavior of other individuals

StigmergyExample.

PSO and ACO Algorithms• Two Common SI Algorithms

• Ant Colony Optimization• Particle Swarm Optimization

Cont. PSO• 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.

Cont. PSO• 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.

• Inspiration: Swarms of Bees, Flocks of Birds, Schools of Fish.

Particle Optimization Technique searching robots

Cont. ACO• ACO

• Optimization Technique Proposed by Marco Dorigo in the early ’90

• Heuristic optimization method inspired by biological systems

• Multi-agent approach for solving difficult combinatorial optimization problems

• Has become new and fruitful research area

Cont. ACO

Cont. ACO• The way ants find their food in shortest path is interesting.

• Ants secrete pheromones to remember their path.

• These pheromones evaporate with time.

• Whenever an ant finds food , it marks its return journey with pheromones.

Cont. ACO• Pheromones evaporate faster on longer paths. (Evaporation)

• Shorter paths serve as the way to food for most of the other ants.

• The shorter path will be reinforced by the pheromones further. (Reinforcement)

• Finally , the ants arrive at the shortest path. (Establishment)

Ant Colony Optimization on Traveling Salesman Pro.

Applications and Recent Developments• Some applications Uses S.I Algorithms :

• Movie effects• Lord of the Rings

• Network Routing• ACO Routing

• Swarm Robotics• Swarm bots

MoviesUsedSwarm Intelligence

Cont. Applications and Recent DevelopmentsOther Recent developed• Human tremor analysis

• Human performance assessment

• Ingredient mix optimization

Cont. Applications and Recent DevelopmentsOther Recent developed• Evolving neural networks to solve problems

• 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

Advantages and Disadvantages• ADVANTAGES:

• The systems are scalable because the same control architecture can be applied to a couple of agents or thousands of agents

• The systems are flexible because agents can be easily added or removed without influencing the structure

Advantages and Disadvantages• ADVANTAGES:

• The systems are robust because agents are simple in design, the reliance on individual agents is small, and failure of a single agents has little impact on the system’s performance

• The systems are able to adapt to new situations easily

Cont. Advantages and Disadvantages• DISADVANTAGES

• 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.

Cont. Advantages and Disadvantages• DISADVANTAGES

• Unpredictable – The complexity of a swarm system leads to unforeseeable results.

• Non-understandable – Sequential systems are understandable; complex adaptive systems, instead, are a jumble of intersecting logic.

• Non-immediate – complex swarm systems with rich hierarchies take time. The more complex the swarm, the longer it takes to shift states

Conclusion• SI provides heuristics to solve difficult optimization problems.

• Has wide variety of applications.

• Basic philosophy of Swarm Intelligence : Observe the behaviour of social animals and try to mimic those animals on computer systems.

• Basic theme of Natural Computing: Observe nature, mimic nature.

References• Reynolds, C. W. (1987) Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics, 21(4) (SIGGRAPH '87 Conference Proceedings) pages 25-34.

• James Kennedy, Russell Eberhart. Particle Swarm Optimization, IEEE Conf. on Neural networks – 1995

• www.adaptiveview.com/articles/ ipsop1• Ruud Schoonderwoerd, Owen Holland, Janet Bruten - 1996. Ant like agents for load balancing in telecommunication networks, Adaptive behavior, 5(2).

References• A Bee Algorithm for Multi-Agents System-Lemmens ,Steven . Karl Tuyls, Ann Nowe -2007

• Swarm Intelligence – Literature Overview, Yang Liu , Kevin M. Passino. 2000.

• www.wikipedia.org• The ACO metaheuristic: Algorithms, Applications, and Advances. Marco Dorigo and Thomas Stutzle-Handbook of metaheuristics, 2002.

• Ant Algorithms for Discrete Optimization Artificial Life• M.Dorigo, M.Birattari, T.Stutzle, Ant colony optimization –Artificial Ants as a computational intelligence technique, IEEE Computational Intelligence Magazine 2006

References• M. Dorigo, G. Di Caro & L. M. Gambardella (1999).• addr:http://iridia.ulb.ac.be/~mdorigo/• Swarm Intelligence, From Natural to Artificial Systems• M. Dorigo, E. Bonabeau, G. Theraulaz• The Yellowjackets of the Northwestern United States, Matthew Kweskin• addr:http://www.evergreen.edu/user/serv_res/research/arthropod/TESCBiota/Vespidae/Kweskin97/main.htm

• Entomology & Plant Pathology, Dr. Michael R. Williams addr:http://www.msstate.edu/Entomology/GLOWORM/GLOW1PAGE.html

• Urban Entomology Program, Dr. Timothy G. Myles addr:http://www.utoronto.ca/forest/termite/termite.htm

References• Dorigo, Marco and Stützle, Thomas. (2004) Ant Colony Optimization,Cambridge, MA: The MIT Press.

• Dorigo, Marco, Gambardella, Luca M., Middendorf, Martin. (2002) “Guest Editorial,” IEEE Transactions on Evolutionary Computation, 6(4): 317-320.

• Ant Colony Optimization by Marco Dorigo and Thomas Stϋtzle, The MIT Press, 2004

• Swarm Intelligence by James Kennedy and Russell Eberhart with YuhuiShi, Morgan Kauffmann Publishers, 2001

• Advances in Applied Artificial Intelligence edited by John Fulcher, IGI Publishing, 2006

• Data Mining: A Heuristic Approach by Hussein Abbass, Ruhul Sarker, and Charles Newton, IGI Publishing, 2002