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Swarm intelligence 1

Swarm intelligence

Swarm intelligence (SI) is the collective behaviour of decentralized, self-organized systems, natural or artificial.

The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing

Wang in 1989, in the context of cellular robotic systems.[1]

SI systems are typically made up of a population of simple agents or boids interacting locally with one another and

with their environment. The agents follow very simple rules, and although there is no centralized control structure

dictating how individual agents should behave, local, and to a certain degree random, interactions between such

agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of

SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more

general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.

Example algorithms

Ant colony optimization

Ant colony optimization (ACO) is a class of optimization algorithms modeled on the actions of an ant colony. ACO

methods are useful in problems that need to find paths to goals. Artificial 'ants'simulation agentslocate optimal

solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones

directing each other to resources while exploring their environment. The simulated 'ants' similarly record their

positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.[2]

One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey

bee.

River formation dynamics

River formation dynamics (RFD)[3]

is an heuristic method similar to ant colony optimization (ACO). In fact, RFD

can be seen as a gradient version of ACO, based on copying how water forms rivers by eroding the ground and

depositing sediments. As water transforms the environment, altitudes of places are dynamically modified, and

decreasing gradients are constructed. The gradients are followed by subsequent drops to create new gradients,

reinforcing the best ones. By doing so, good solutions are given in the form of decreasing altitudes. This method has

been applied to solve different NP-complete problems (for example, the problems of finding a minimum distances

tree and finding a minimum spanning tree in a variable-cost graph[4]

). The gradient orientation of RFD makes it

specially suitable for solving these problems and provides a good tradeoff between finding good results and not

spending much computational time. In fact, RFD fits particularly well for problems consisting in forming a kind of

covering tree.[5]

Particle swarm optimization

Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best

solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and

seeded with an initial velocity, as well as a communication channel between the particles[6]

[7]

. Particles then move

through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time,

particles are accelerated towards those particles within their communication grouping which have better fitness

values. The main advantage of such an approach over other global minimization strategies such as simulated

annealing is that the large number of members that make up the particle swarm make the technique impressivelyresilient to the problem of local minima.
http://en.wikipedia.org/w/index.php?title=Simulated_annealinghttp://en.wikipedia.org/w/index.php?title=Local_minimahttp://en.wikipedia.org/w/index.php?title=Fitness_%28biology%29http://en.wikipedia.org/w/index.php?title=Simulated_annealinghttp://en.wikipedia.org/w/index.php?title=Local_minimahttp://en.wikipedia.org/w/index.php?title=Simulated_annealinghttp://en.wikipedia.org/w/index.php?title=Local_minimahttp://en.wikipedia.org/w/index.php?title=Local_minimahttp://en.wikipedia.org/w/index.php?title=Simulated_annealinghttp://en.wikipedia.org/w/index.php?title=Simulated_annealinghttp://en.wikipedia.org/w/index.php?title=Fitness_%28biology%29http://en.wikipedia.org/w/index.php?title=Velocityhttp://en.wikipedia.org/w/index.php?title=Global_optimizationhttp://en.wikipedia.org/w/index.php?title=Particle_swarm_optimizationhttp://en.wikipedia.org/w/index.php?title=River_formation_dynamicshttp://en.wikipedia.org/w/index.php?title=Honey_beehttp://en.wikipedia.org/w/index.php?title=Honey_beehttp://en.wikipedia.org/w/index.php?title=Bees_algorithmhttp://en.wikipedia.org/w/index.php?title=Pheromonehttp://en.wikipedia.org/w/index.php?title=Parameter_spacehttp://en.wikipedia.org/w/index.php?title=Ant_colonyhttp://en.wikipedia.org/w/index.php?title=Algorithmhttp://en.wikipedia.org/w/index.php?title=Optimization_%28mathematics%29http://en.wikipedia.org/w/index.php?title=Ant_colony_optimizationhttp://en.wikipedia.org/w/index.php?title=Swarm_roboticshttp://en.wikipedia.org/w/index.php?title=Robothttp://en.wikipedia.org/w/index.php?title=Shoaling_and_schoolinghttp://en.wikipedia.org/w/index.php?title=Bacteria%23Growth_and_reproductionhttp://en.wikipedia.org/w/index.php?title=Herdinghttp://en.wikipedia.org/w/index.php?title=Flocking_%28behavior%29http://en.wikipedia.org/w/index.php?title=Ant_colonyhttp://en.wikipedia.org/w/index.php?title=Emergencehttp://en.wikipedia.org/w/index.php?title=Boidshttp://en.wikipedia.org/w/index.php?title=Intelligent_agenthttp://en.wikipedia.org/w/index.php?title=Cellular_automatonhttp://en.wikipedia.org/w/index.php?title=Gerardo_Benihttp://en.wikipedia.org/w/index.php?title=Artificial_intelligencehttp://en.wikipedia.org/w/index.php?title=Self_organizationhttp://en.wikipedia.org/w/index.php?title=Decentralizationhttp://en.wikipedia.org/w/index.php?title=Collective_behaviour


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Stochastic diffusion search

Stochastic diffusion search (SDS) is an agent-based probabilistic global search and optimization technique best

suited to problems where the objective function can be decomposed into multiple independent partial-functions.

Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective

function parameterised by the agent's current hypothesis. In the standard version of SDS such partial function

evaluations are binary, resulting in each agent becoming active or inactive. Information on hypotheses is diffusedacross the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS

agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running

procedure observed in some species of ant. A positive feedback mechanism ensures that, over time, a population of

agents stabilise around the global-best solution. SDS is both an efficient and robust search and optimisation

algorithm, which has been extensively mathematically described.

Gravitational search algorithm

Gravitational search algorithm (GSA) is constructed based on the law of Gravity and the notion of mass interactions.

The GSA algorithm uses the theory of Newtonian physics and its searcher agents are the collection of masses. In

GSA, we have an isolated system of masses. Using the gravitational force, every mass in the system can see the

situation of other masses. The gravitational force is therefore a way of transferring information between different

masses.(Rashedi et.al, 2009)

Intelligent Water Drops

Intelligent Water Drops algorithm (IWD) is a swarm-based nature-inspired optimization algorithm, which has been

inspired from natural rivers and how they find almost optimal paths to their destination.These near optimal or

optimal paths follow from actions and reactions occurring among the water drops and the water drops with their

riverbeds. In the IWD algorithm, several artificial water drops cooperate to change their environment in such a way

that the optimal path is revealed as the one with the lowest soil on its links. The solutions are incrementally

constructed by the IWD algorithm. Consequently, the IWD algorithm[8]

is generally a constructive population-based

optimization algorithm.

Charged System Search

Charged System Search (CSS)[9]

is a new optimization algorithm based on some principles from physics and

mechanics. CSS utilizes the governing laws of Coulomb and Gauss from electrostatics and the Newtonian laws of

mechanics. CSS is a multi-agent approach in which each agent is a Charged Particle (CP). CPs can affect each other

based on their fitness values and their separation distances. The quantity of the resultant force is determined by using

the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. CSS is

applicable to all optimization fields; especially it is suitable for non-smooth or non-convex domains. This algorithmprovides a good balance between the exploration and the exploitation paradigms of the algorithm which can

considerably improve the efficiency of the algorithm and therefore the CSS also can be considered as a good global

and local optimizer simultaneously.

Applications

Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating

swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital

swarm for self assembly and interferometry. NASA is investigating the use of swarm technology for planetary

mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey[10]

discusses the possibility of using swarm

intelligence to control nanobots within the body for the purpose of killing cancer tumors.
http://en.wikipedia.org/w/index.php?title=European_Space_Agencyhttp://en.wikipedia.org/w/index.php?title=NASAhttp://en.wikipedia.org/w/index.php?title=M._Anthony_Lewis_%28roboticist%29http://en.wikipedia.org/w/index.php?title=George_A._Bekeyhttp://en.wikipedia.org/w/index.php?title=NASAhttp://en.wikipedia.org/w/index.php?title=M._Anthony_Lewis_%28roboticist%29http://en.wikipedia.org/w/index.php?title=George_A._Bekeyhttp://en.wikipedia.org/w/index.php?title=George_A._Bekeyhttp://en.wikipedia.org/w/index.php?title=M._Anthony_Lewis_%28roboticist%29http://en.wikipedia.org/w/index.php?title=NASAhttp://en.wikipedia.org/w/index.php?title=European_Space_Agencyhttp://en.wikipedia.org/w/index.php?title=Gravitational_search_algorithmhttp://en.wikipedia.org/w/index.php?title=Stigmergyhttp://en.wikipedia.org/w/index.php?title=Stochastic_diffusion_search


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Crowd simulation

Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds.

Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering,

realistically depicting the movements of groups of fish and birds using the Boids system. Tim Burton's Batman

Returns also made use of swarm technology for showing the movements of a group of bats. The Lord of the Rings

film trilogy made use of similar technology, known as Massive, during battle scenes. Swarm technology is

particularly attractive because it is cheap, robust, and simple.

There is hardly anything more crowded than the interior of a commercial aircraft. Swarm theory has been used in

aircraft boarding. Over the years airlines have used computer simulations of passengers boarding a plane. Southwest

Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules

to evaluate boarding times using various boarding methods.(Miller, 2010, xii-xviii)[11]

.

Ant-based routing

The use of Swarm Intelligence in Telecommunication Networks has also been researched, in the form of Ant Based

Routing. This was pioneered separately by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number ofvariations since. Basically this uses a probabilistic routing table rewarding/reinforcing the route successfully

traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards,

reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric

network and couples the two directions together; forwards reinforcement rewards a route before the outcome is

known (but then you pay for the cinema before you know how good the film is). As the system behaves

stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media

and new technologies have the potential to change the threshold for collective action due to swarm intelligence

(Rheingold: 2002, P175).

Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. At Southwest Airlines a

software program uses swarm theory, or swarm intelligence -- the idea that a colony of ants works better than onealone. Each pilot acts like an ant searching for the best airport gate. "The pilot learns from his experience what's the

best for him, and it turns out that that's the best solution for the airline," Dr. Douglas A. Lawson explains. As a result,

the "colony" of pilots always go to gates they can arrive and depart quickly. The program can even alert a pilot of

plane back-ups before they happen. "We can anticipate that it's going to happen, so we'll have a gate available," Dr.

Lawson says[12]

.

References in popular culture

Swarm intelligence-related concepts and references can be found throughout popular culture, frequently as some

form of collective intelligence or group mind involving far more agents than used in current applications.

A popular Soviet SF writer Sever Gansovsky in his short story "The Host of Bay" (1962) described a sea monster

consisting of microscopical particles which can self-organize themselves into a unity for hunt.

Talus[13]

by Erol Ozan deals with an exotic life form powered by swarm intelligence emerging from the telepathic

minds of kalonoro, Madagascar's cryptic hominids.

The Borg in Star Trek

In 28 Days Later, the Infected have some form of hive mind, in that they have some form of intelligence which

enables them to hunt down and kill the survivors of the Rage Virus. This "intelligence" is shown mainly when the

Infected attack in large packs, as they focus all their attention on the human survivors, and will not stop until the

survivors are dead or Infected.

The Zerg of the Starcraft universe demonstrate such concepts when in groups and enhanced by the psychiccontrol of taskmaster breeds; the Zerg's original leader, the Overmind, was generally thought of not as in
http://en.wikipedia.org/w/index.php?title=Zerghttp://en.wikipedia.org/w/index.php?title=Starcraft_%28series%29http://en.wikipedia.org/w/index.php?title=Zerg_Overmind%23The_Overmindhttp://en.wikipedia.org/w/index.php?title=Zerghttp://en.wikipedia.org/w/index.php?title=Starcraft_%28series%29http://en.wikipedia.org/w/index.php?title=Zerg_Overmind%23The_Overmindhttp://en.wikipedia.org/w/index.php?title=Zerg_Overmind%23The_Overmindhttp://en.wikipedia.org/w/index.php?title=Starcraft_%28series%29http://en.wikipedia.org/w/index.php?title=Zerghttp://en.wikipedia.org/w/index.php?title=Zombiehttp://en.wikipedia.org/w/index.php?title=28_Days_Laterhttp://en.wikipedia.org/w/index.php?title=Star_Trekhttp://en.wikipedia.org/w/index.php?title=Borg_%28Star_Trek%29http://en.wikipedia.org/w/index.php?title=Sever_Gansovskyhttp://en.wikipedia.org/w/index.php?title=Group_mindhttp://en.wikipedia.org/w/index.php?title=Collective_intelligencehttp://en.wikipedia.org/w/index.php?title=Douglas_A._Lawsonhttp://en.wikipedia.org/w/index.php?title=Hewlett_Packardhttp://en.wikipedia.org/w/index.php?title=Ant_Based_Routinghttp://en.wikipedia.org/w/index.php?title=Ant_Based_Routinghttp://en.wikipedia.org/w/index.php?title=Telecommunications_networkhttp://en.wikipedia.org/w/index.php?title=Douglas_A._Lawsonhttp://en.wikipedia.org/w/index.php?title=Massive_%28software%29http://en.wikipedia.org/w/index.php?title=The_Lord_of_the_Rings_film_trilogyhttp://en.wikipedia.org/w/index.php?title=The_Lord_of_the_Rings_film_trilogyhttp://en.wikipedia.org/w/index.php?title=Batman_Returnshttp://en.wikipedia.org/w/index.php?title=Batman_Returnshttp://en.wikipedia.org/w/index.php?title=Boidshttp://en.wikipedia.org/w/index.php?title=Stanley_and_Stella_in:_Breaking_the_Icehttp://en.wikipedia.org/w/index.php?title=Crowd_simulation


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individual with a central intelligence to control others but instead as a being which embodies the hive mind of

Zerg that mentally linked

Science fiction writer Olaf Stapledon may have been the first to discuss swarm intelligences equal or superior to

humanity. InLast and First Men (1931), a swarm intelligence from Mars consists of tiny individual cells that

communicate with each other by radio waves; in Star Maker (1937) swarm intelligences founded numerous

civilizations, composed of flocks of birds or, as the universe cools, energy-efficient swarms of burrowing slugs;

ultimately all intelligence in the universe swarms into a single entity barely capable of perceiving the Supreme

Moment.

Decipherby Stel Pavlou deal with the swarm intelligence of nanobots that guard against intruders in Atlantis.

Prey, by Michael Crichton deals with the danger of nanobots escaping from human control and developing a

swarm intelligence.

Wyrm, a novel by Mark Fabi deals with a virus developing emergent intelligence on the Internet

Swarm, a short story by Bruce Sterling about a mission undertaken by a faction of humans, to understand and

exploit a space-faring swarm intelligence.

The Hacker and the Ants, a book by Rudy Rucker on AI ants within a virtual environment

Ygramul, the Many - an intelligent being consisting of a swarm of many wasp-like insects, a character in thenovel The Neverending Story written by Michael Ende. Ygramul is also mentioned in a scientific paper Flocks,

Herds, and Schools[14]

written by Knut Hartmann (Computer Graphics and Interactive Systems

Otto-von-Guericke-University of Magdeburg).

Allucination, a short story by Isaac Asimov about an alien insect-like swarm, capable of organization and

provided with a sort of swarm intelligence.

The Invincible science fiction novel by Stanisaw Lem where a human spaceship finds an intelligent behavior in a

flock of small particles that were able to defend themselves against what they found as a menace.

In The Matrix movies, the robotic sentinels exhibit signs of swarm intelligence. Additionally, in The Matrix

Revolutions, a machine called theDeus Ex Machina uses a swarm of thousands of insect-like robots to form a

giant animated face. In the anime, Soukou no Strain, unmanned robotic drones known as TUMORS display signs of swarm

intelligence as they attack in groups.

In the dramatic novel and subsequent mini-series: "The Andromeda Strain" by author Michael Crichton, A virus

from the future communicates between individual cells and displays the ability to think and react individually and

as a whole, and as such displays a semblance of "swarm intelligence".

In the computer RPG Planescape: Torment, the protagonist encounters monsters called cranium rats, which grow

more intelligent in larger groups. The game features encounters with individual rats, small groups, and a very

large group that identifies itself as "Many-as-One".

In the video game Mass Effect, a galactic race known as the Quarians created a race of humanoid sentient

machines known as the Geth. Geth are said to possess limited artificial intelligence and problem solving skillswhen alone, but automatically network with each other when in groups. This means that the machine's ability to

solve problems increases in correlation with the size of the group.

The television series Stargate SG-1 has entities known as replicators. Individually they only existed for their own

survival, consuming and avoiding danger. However they also were able to link up all other replicators in the area.

This made it possible like the Geth in Mass Effect to have vastly superior intelligence than would be expected

from such a basic entity. This allowed them to defeat opponents that individually dwarfed them by a large extent

in terms of intellect and size due to cluster computing.

In the Commonwealth Saga by Peter F. Hamilton, the Dyson Aliens exhibit elements of swarm intelligence with

their linked immotile brains and motile ancillary units.

The novellaHuman Readable by Cory Doctorow is set in a future where ant-based routing is widely used fortraffic and communications, and explores their potential drawbacks.
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The science fiction novel, The Swarm, by Frank Schtzing, deals with underwater single celled creatures who act

in unison to destroy humanity.

Notable researchers

Gerardo Beni

Eric Bonabeau

Marco Dorigo

Luca Maria Gambardella

Russell C. Eberhart

James Kennedy

Douglas A. Lawson

Craig Reynolds

Ken Rinaldo

Dervis Karaboga

References

[1] Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems,

Tuscany, Italy, June 2630 (1989)

[2] Ant Colony Optimization by Marco Dorigo and Thomas Sttzle, MIT Press, 2004. ISBN 0-262-04219-3

[3] Using River Formation Dynamics to Design Heuristic Algorithms (http://springerlink.com/content/w2453h31g9527157/) by Pablo

Rabanal, Ismael Rodrguez and Fernando Rubio, Springer, 2007. ISBN 978-3-540-73553-3

[4] Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics (http://springerlink.com/content/hv760378624684u3/)

by Pablo Rabanal, Ismael Rodrguez and Fernando Rubio, Springer, 2008. ISBN 978-3-540-87526-0

[5] A Formal Approach to Heuristically Test Restorable Systems (http://springerlink.com/content/qn024854771052w4/) by Pablo Rabanal,

Ismael Rodrguez and Fernando Rubio, Springer, 2009. ISBN 978-3-642-03465-7

[6] Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization (http://springerlink. com/openurl.

asp?genre=article&doi=10.

1023/A:1016568309421), by Parsopoulos, K.E., Vrahatis, M.N., Natural Computing, 1 (2-3), pp. 235-306, 2002.[7] Particle Swarm Optimization (http://www.iste.co. uk/?searchtext=clerc&ACTION=Search&cat=&ACTION=Search) by Maurice Clerc,

ISTE, ISBN 1-905209-04-5, 2006.

[8] Hamed Shah-Hosseini, The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, (http://www.

inderscience.com/filter. php?aid=22775), International Journal of Bio-Inspired Computation, Vol. 1, Nos. 1/2, pp. 71-79, 2009.

[9] A. Kaveh and S. Talatahari, A Novel Heuristic Optimization Method: Charged System Search, Acta Mechanica, Volume 213, Issues 3-4

Pages 267-289 (2010)

[10] Lewis, M. Anthony, and Bekey, George A. The Behavioral Self-Organization of Nanorobots Using Local Rules. Proceedings of the 1992

IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11] Miller, Peter. 2010. The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision

making, and getting things done. Avery: New York.

[12] Science Daily. 2008 (April 1). "Planes, Trains and Ant Hills: Computer scientists simulate activity of ants to reduce airline delays." World

Wide Web: http://www.sciencedaily.com/videos/2008/0406-planes_trains_and_ant_hills. htm. Retrieved December 1, 2010.

[13] Talus by Erol Ozan. A Novel. http://www.amazon. com/Talus-Erol-Ozan/dp/1452828199

[14] http://wwwisg.cs.uni-magdeburg. de/games/veranstaltungsarchiv/2003_ss/csii/boids. pdf#search=%22Ygramul%20the%20Many%22
http://wwwisg.cs.uni-magdeburg.de/games/veranstaltungsarchiv/2003_ss/csii/boids.pdf#search=%22Ygramul%20the%20Many%22http://www.amazon.com/Talus-Erol-Ozan/dp/1452828199http://www.sciencedaily.com/videos/2008/0406-planes_trains_and_ant_hills.htm.http://www.inderscience.com/filter.php?aid=22775http://www.inderscience.com/filter.php?aid=22775http://www.iste.co.uk/?searchtext=clerc&ACTION=Search&cat=&ACTION=Searchhttp://springerlink.com/openurl.asp?genre=article&doi=10.1023/A:1016568309421http://springerlink.com/openurl.asp?genre=article&doi=10.1023/A:1016568309421http://springerlink.com/content/qn024854771052w4/http://springerlink.com/content/hv760378624684u3/http://springerlink.com/content/w2453h31g9527157/http://en.wikipedia.org/w/index.php?title=Dervis_Karabogahttp://en.wikipedia.org/w/index.php?title=Ken_Rinaldohttp://en.wikipedia.org/w/index.php?title=Craig_Reynolds_%28computer_graphics%29http://en.wikipedia.org/w/index.php?title=Douglas_A._Lawsonhttp://en.wikipedia.org/w/index.php?title=James_Kennedy_%28social_psychologist%29http://en.wikipedia.org/w/index.php?title=Russell_C._Eberharthttp://en.wikipedia.org/w/index.php?title=Luca_Maria_Gambardellahttp://en.wikipedia.org/w/index.php?title=Marco_Dorigohttp://en.wikipedia.org/w/index.php?title=Eric_Bonabeauhttp://en.wikipedia.org/w/index.php?title=Gerardo_Benihttp://en.wikipedia.org/w/index.php?title=Frank_Sch%C3%A4tzinghttp://en.wikipedia.org/w/index.php?title=The_Swarm_%28novel%29


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Bibliography

Swarm Intelligence: From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo and Guy Theraulaz.

(1999) ISBN 0-19-513159-2, complete bibliography (http://bibnetwiki.org/wiki/Swarm_Intelligence)

Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds by Mitchel Resnick. ISBN

0-262-18162-2

Swarm Intelligence by James Kennedy and Russell C. Eberhart. ISBN 1-55860-595-9

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index.jsp?&pName=computer_level1_article&TheCat=1065&path=computer/homepage/April07&

file=softtech.xml&xsl=article.xsl)

- "From Ants to People: an Instinct to Swarm" - NY Times, 11-13-07 (http://www.nytimes.com/2007/11/13/

science/13traff.html?ei=5087&em=&en=2770422853e9f63e&ex=1195102800&pagewanted=print)

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Article Sources and Contributors 7

Article Sources and ContributorsSwarm intelligence Source: http://en.wikipedia.org/w/index.php?oldid=402877829 Contributors: .Koen, 4th-otaku, Abdullahazzam, Abelaragorn, Aelyan, Ajithabraham, Akira.exe, Alpha for

knowledge, Andrevan, Apankrat, Archange56, Arthur Rubin, Ashmoo, Bahriyebasturk, Binksternet, Blooy, Calimo, Cardiffbaybee, Chris the speller, Daryakav, Deodar, Dobri2, DrOxacropheles,

Dragentsheets, Dreadstar, DynamicUno, DynamoDegsy, Ehrami, Epipelagic, Etet245, Everyking, Gadlen, GeeJo, Giftlite, Gottfried1646, Gtrmp, Hydnjo, IDSIAupdate, Iciac, Ire and curses, J

ham3, Jacen137, Jamesontai, Jbapowell, Jbw2, Jiuguang Wang, Joerite, Josephgec, Jyril, K.menin, K3rb, Kanags, Keraunos, Khalid hassani, Kjl, Knowlengr, KrakatoaKatie, Krinndnz, Kumioko,

LuckyInWaco, M0nstr42, Manticore, Marek69, Markybish, Maurice Carbonaro, Mdd, Mdorigo, Michael Hardy, Michaelbusch, Miym, Mneser, Moxon, MuffledThud, MuthuKutty, Myleslong,

N2e, NerdyNSK, Oleg Alexandrov, Ouc, Physis, Possum, Prometheus1000, Pudor, Quailwood, Quiddity, Rene Mas, Rewinn, Ronaldo, Ronz, Roregan, Scifun, Seaphoto, Sergey539, Sheltim,

Shervinafshar, Siamaktalat, Simoneau, SiobhanHansa, Sockettome, Squid603, StaticDust, Stefanomione, Tarotcards, Tcooling, The Thing That Should Not Be, TheDegreeCollector,TheMidnighters, Turian, Ultimamultima, Van Parunak, Waldir, Warofdreams, Warreed, Werdan7, Xino2005, Yserbius, Zeeshan.Usmani, Zwgeem, 1, 305 anonymous edits

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