Methods | Statistics | Clinical | Educational | Industrial | Professional items | World psychology |
Swarm behaviour, or swarming, is a collective behaviour exhibited by animals of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. As a term, swarming is applied particularly to insects, but can also be applied to any other animal that exhibits swarm behaviour. The term flocking is usually used to refer specifically to swarm behaviour in birds, herding to refer to swarm behaviour in quadrupeds, shoaling or schooling to refer to swarm behaviour in fish.
From a more abstract point of view, swarm behaviour is the collective motion of a large number of self-propelled entities. From the perspective of the mathematical modeller, it is an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination.
Swarm behaviour was first simulated on a computer in 1986 with the simulation program boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The model was originally designed to mimic the flocking behaviour of birds, but it can be applied also to schooling fish and other swarming entities.
|30px External images|
- See also: Collective animal behaviour
The simplest mathematical models of animal swarms generally represent individual animals as following three rules:
- Move in the same direction as your neighbours
- Remain close to your neighbours
- Avoid collisions with your neighbours
The boids computer program, created by Craig Reynolds in 1986, simulates swarm behaviour following the above rules. Many subsequent and current models use variations on these rules, often implementing them by means of concentric "zones" around each animal. In the zone of repulsion, very close to the animal, the focal animal will seek to distance itself from its neighbours to avoid collision. Slightly further away, in the zone of alignment, the focal animal will seek to align its direction of motion with its neighbours. In the outermost zone of attraction, which extends as far away from the focal animal as it is able to sense, the focal animal will seek to move towards a neighbour.
The shape of these zones will necessarily be affected by the sensory capabilities of the given animal. For example the visual field of a bird does not extend behind its body. Fish rely on both vision and on hydrodynamic perceptions relayed through their lateral line, while Antarctic krill rely both on vision and hydrodynamic signals relayed through antennae.
However recent studies of starling flocks have shown that each bird modifies its position, relative to the six or seven animals directly surrounding it, no matter how close or how far away those animals are. Interactions between flocking starlings are thus based on a topological rule rather than a metric rule. It remains to be seen whether this applies to other animals.
- Main article: Agent-based model in biology
- Mach R, Schweitzer F (2003). Multi-agent model of biological swarming. Lecture Notes in Computer Science,'' 2801: 810–820.
- Main article: Swarm intelligence
Swarm intelligence 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.
Swarm intelligence 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 behaviour, unknown to the individual agents.
Swarm intelligence research is multidisciplinary. It can be divided into natural swarm research studying biological systems and artificial swarm research studying human artefacts. There is also a scientific stream attempting to model the swarm systems themselves and understand their underlying mechanisms, and an engineering stream focused on applying the insights developed by the scientific stream to solve practical problems in other areas.
- Main article: Emergence
The concept of emergence—that the properties and functions found at a hierarchical level are not present and are irrelevant at the lower levels–is often a basic principle behind self-organizing systems. An example of self-organization in biology leading to emergence in the natural world occurs in ant colonies. The queen does not give direct orders and does not tell the ants what to do. Instead, each ant reacts to stimuli in the form of chemical scent from larvae, other ants, intruders, food and build up of waste, and leaves behind a chemical trail, which, in turn, provides a stimulus to other ants. Here each ant is an autonomous unit that reacts depending only on its local environment and the genetically encoded rules for its variety of ant. Despite the lack of centralized decision making, ant colonies exhibit complex behaviour and have even been able to demonstrate the ability to solve geometric problems. For example, colonies routinely find the maximum distance from all colony entrances to dispose of dead bodies.
- Main article: Stigmergy
A further key concept in the field of swarm intelligence is stigmergy. Stigmergy is a mechanism of indirect coordination between agents or actions. The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent. In that way, subsequent actions tend to reinforce and build on each other, leading to the spontaneous emergence of coherent, apparently systematic activity. Stigmergy is a form of self-organization. It produces complex, seemingly intelligent structures, without need for any planning, control, or even direct communication between the agents. As such it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even awareness of each other.
Swarm algorithms follow a Lagrangian approach or an Eulerian approach. The Eulerian approach views the swarm as a field, working with the density of the swarm and deriving mean field properties. It is a hydrodynamic approach, and can be useful for modelling the overall dynamics of large swarms. However, most models work with the Lagrangian approach, which is an agent-based model following the individual agents (points or particles) that make up the swarm. Individual particle models can follow information on heading and spacing that is lost in the Eulerian approach.
Ant colony optimizationEdit
- Main article: Ant colony optimization
|30px External images|
|16px||Swarmanoid robots find shortest path over double bridge|
Ant colony optimization is a widely used algorithm which was inspired by the behaviours of ants, and has been effective solving discrete optimization problems related to swarming. The algorithm was initially proposed by Marco Dorigo in 1992, and has since been diversified to solve a wider class of numerical problems. Species that have multiple queens may have a queen leaving the nest along with some workers to found a colony at a new site, a process akin to swarming in honeybees.
- Ants are behaviourally unsophisticated; collectively they perform complex tasks. Ants have highly developed sophisticated sign-based communication.
- Ants communicate using pheromones; trails are laid that can be followed by other ants.
- Routing problem ants drop different pheromones used to compute the "shortest" path from source to destination(s).
- Rauch EM, Millonas MM and Chialvo DR (1995) "Pattern formation and functionality in swarm models"] Physics Letters A, 207: 185. arXiv:adap-org/9507003
- Main article: Self-propelled particles
|30px External videos|
|16px|| SPP model interactive simulation|
– needs Java
Self-propelled particles (SPP) is a concept introduced in 1995 by Vicsek and Couzin et al as a special case of the boids model introduced in 1986 by Reynolds. A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood.
Simulations demonstrate that a suitable "nearest neighbour rule" eventually results in all the particles swarming together, or moving in the same direction. This emerges, even though there is no centralized coordination, and even though the neighbours for each particle constantly change over time (see the interactive simulation in the box on the right). SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.
Particle swarm optimizationEdit
- Main article: Particle swarm optimization
Particle swarm optimization is another algorithm widely used to solve problems related to swarms. It was developed in 1995 by Kennedy and Eberhart and was first aimed at simulating the social behaviour and choreography of bird flocks and fish schools. The algorithm was simplified and it was observed to be performing optimization. The system initially seeds a population with random solutions. It then searches in the problem space through successive generations using stochastic optimization to find the best solutions. The solutions it finds are called particles. Each particle stores its position as well as the best solution it has achieved so far. The particle swarm optimizer tracks the best local value obtained so far by any particle in the local neighbourhood. The remaining particles then move through the problem space following the lead of the optimum particles. At each time iteration, the particle swarm optimiser accelerates each particle toward its optimum locations according to simple mathematical rules. Particle swarm optimization has been applied in many areas. It has few parameters to adjust, and a version that works well for a specific applications can also work well with minor modifications across a range of related applications. A book by Kennedy and Eberhart describes some philosophical aspects of particle swarm optimization applications and swarm intelligence. An extensive survey of applications is made by Poli.
Examples of biological swarming are found in bird flocks, fish schools, insect swarms, bacteria swarms, molds, molecular motors, quadruped herds and people.
- Swarm Theory National Geographic. Feature article, July 2007.
- Beekman M, Sword GA and Simpson SK (2008) Biological Foundations of Swarm Intelligence. In Swarm intelligence: introduction and applications, Eds Blum C and Merkle D. シュプリンガー・ジャパン株式会社, Page 3–43. ISBN 978-3-540-74088-9
- Parrish JK, Edelstein-Keshet L (1999). Complexity, pattern and evolutionary trade-offs in animal aggregation. Science 284 (5411): 99–101.
- Synchronous spawning
- External fertilization
The behaviour of insects that live in colonies, such as ants, bees, wasps and termites, has always been a source of fascination for children, naturalists and artists. Individual insects seems to do their own thing without any central control, yet the colony as a whole behaves in a highly coordinated manner. Researchers have found that cooperation at the colony level is largely self-organized. The group coordination that emerges is often just a consequence of the way individuals in the colony interact. These interactions can be remarkably simple, such as one ant merely following the trail left by another ant. Yet put together, the cumulative effect of such behaviours can solve highly complex problems, such as locating the shortest route in a network of possible paths to a food source. The organised behaviour that emerges in this way is sometimes called swarm intelligence.
Individual ants do not exhibit complex behaviours, yet a colony of ants collectively achieves complex tasks such as constructing nests, taking care of their young, building bridges and foraging for food. A colony of ants can locate the food source nearest their nest without any individual ant possessing any knowledge about that. If there are two paths from the ant nest to a food source, then the colony usually selects the shorter path. This is because individual ants lay down chemical trails as they move, a pheromone which is then followed by other ants. The ants that first return to the nest from the food source are more likely to be those that took the shorter path. More ants then retrace the shorter path, reinforcing the pheromone trail. In this way, "the ants collectively solve an optimization problem using emergent computation."
The successful techniques used by ant colonies have been studied in computer science and robotics to produce distributed and fault-tolerant systems for solving problems. This area of biomimetics has led to studies of ant locomotion, search engines that make use of "foraging trails", fault-tolerant storage and networking algorithms.
- Main article: Bees algorithm
When a honey bee swarm emerges from a hive they do not fly far at first. They may gather in a tree or on a branch only a few meters from the hive. There, they cluster about the queen and send 20 -50 scout bees out to find a suitable new nest locations. The scout bees are the most experienced foragers in the cluster. An individual scout returning to the cluster promotes a location she found. She uses a dance similar to the waggle dance to indicate direction and distance to others in the cluster. The more excited she is about her findings the more excitedly she dances. If she can convince other scouts to check out the location she found, they may take off, check out the proposed site and promote the site further upon their return. Several different sites may be promoted by different scouts at first. After several hours and sometimes days, slowly a favourite location emerges from this decision making process. When all scouts agree on a final location the whole cluster takes off and flies to it. A swarm may fly for a kilometre or more to the scouted out location. This collective decision making process is remarkably successful in identifying the most suitable new nest site and keeping the swarm intact. A good nest site has to be large enough to accommodate the swarm (about 15 litres in volume), has to be well protected from the elements, receive a certain amount of warmth from the sun and be not infested with ants.
Similar to ants, cockroaches leave chemical trails in their faeces as well as emitting airborne pheromones for swarming and mating. Other cockroaches will follow these trails to discover sources of food and water, and also discover where other cockroaches are hiding. Thus, cockroaches can exhibit emergent behaviour, in which group or swarm behaviour emerges from a simple set of individual interactions.
Cockroaches are mainly nocturnal and will run away when exposed to light. A study tested the hypothesis that cockroaches use just two pieces of information to decide where to go under those conditions: how dark it is and how many other cockroaches there are. The study conducted by José Halloy and colleagues at the Free University of Brussels and other European institutions created a set of tiny robots that appear to the roaches as other roaches and can thus alter the roaches' perception of critical mass. The robots were also specially scented so that they would be accepted by the real roaches.
- See also: Marching locusts
Locusts are the swarming phase of the short-horned grasshoppers of the family Acrididae. Some species can breed rapidly under suitable conditions and subsequently become gregarious and migratory. They form bands as nymphs and swarms as adults—both of which can travel great distances, rapidly stripping fields and greatly damaging crops. The largest swarms can cover hundreds of square miles and contain billions of locusts. A locust can eat its own weight (about 2 grams) in plants every day. That means one million locusts can eat about one ton of food each day, and the largest swarms can consume over 100,000 tonnes each day.
Swarming in locusts has been found to be associated with increased levels of serotonin which causes the locust to change colour, eat much more, become mutually attracted, and breed much more easily. Researchers propose that swarming behaviour is a response to overcrowding and studies have shown that increased tactile stimulation of the hind legs or, in some species, simply encountering other individuals causes an increase in levels of serotonin. The transformation of the locust to the swarming variety can be induced by several contacts per minute over a four-hour period.
An individual locust's response to a loss of alignment in the group appears to increase the randomness of its motion, until an aligned state is again achieved. This noise-induced alignment appears to be an intrinsic characteristic of collective coherent motion.
- Main article: Insect migration
- See also: Lepidoptera migration
Insect migration is the seasonal movement of insects, particularly those by species of dragonflies, beetles, butterflies and moths. The distance can vary from species to species, but in most cases these movements involve large numbers of individuals. In some cases the individuals that migrate in one direction may not return and the next generation may instead migrate in the opposite direction. This is a significant difference from bird migration.
Monarch butterflies are especially noted for their lengthy annual migration. In North America they make massive southward migrations starting in August until the first frost. A northward migration takes place in the spring. The monarch is the only butterfly that migrates both north and south as the birds do on a regular basis. But no single individual makes the entire round trip. Female monarchs deposit eggs for the next generation during these migrations. The length of these journeys exceeds the normal lifespan of most monarchs, which is less than two months for butterflies born in early summer. The last generation of the summer enters into a non-reproductive phase known as diapause and may live seven months or more. During diapause, butterflies fly to one of many overwintering sites. The generation that overwinters generally does not reproduce until it leaves the overwintering site sometime in February and March. It is the second, third and fourth generations that return to their northern locations in the United States and Canada in the spring. How the species manages to return to the same overwintering spots over a gap of several generations is still a subject of research; the flight patterns appear to be inherited, based on a combination of the position of the sun in the sky and a time-compensated Sun compass that depends upon a circadian clock that is based in their antennae.
- Main article: Flocking (behaviour)
- Nagy M, Akos Zs, Biro D and Vicsek T (2010) "Hierarchical group dynamics in pigeon flocks" Nature, 464: 890-893. Download pdf Supplementary pdf
- Main article: Bird migration
- See also: Reverse migration
Approximately 1800 of world's 10,000 bird species are long-distance migrants. The primary motivation for migration appears to be food; for example, some hummingbirds choose not to migrate if fed through the winter. Also, the longer days of the northern summer provide extended time for breeding birds to feed their young. This helps diurnal birds to produce larger clutches than related non-migratory species that remain in the tropics. As the days shorten in autumn, the birds return to warmer regions where the available food supply varies little with the season. These advantages offset the high stress, physical exertion costs, and other risks of the migration such as predation.
Many, if not most, birds migrate in flocks. For larger birds, flying in flocks reduces energy costs. Geese in a V-formation may conserve 12–20% of the energy they would need to fly alone. Red knots and dunlins were found in radar studies to fly 5 km per hour faster in flocks than when they were flying alone. Flocks of birds save energy when flying together, much in the way that bicyclists draft one another in a peloton. Geese flying in a Vee formation save energy by flying in the updraft of the wingtip vortex generated by the previous animal in the formation. Thus, the birds flying behind do not need to work as hard to achieve lift. Studies show that birds in a V formation place themselves roughly at the optimum distance predicted by simple aerodynamic theory.
The V formation greatly boosts the efficiency and range of flying birds, particularly over long migratory routes. All the birds except the first fly in the upwash from the wingtip vortices of the bird ahead. The upwash assists each bird in supporting its own weight in flight, in the same way a glider can climb or maintain height indefinitely in rising air. In a V formation of 25 members, each bird can achieve a reduction of induced drag by up to 65% and as a result increase their range by 71%. The birds flying at the tips and at the front are rotated in a timely cyclical fashion to spread flight fatigue equally among the flock members. The formation also makes communication easier and allows the birds to maintain visual contact with each other.
|30px External videos|
|16px|| Lobster Migration scene|
– from The Trials of Life
Other animals may use similar drafting techniques when migrating. Lobsters, for example, migrate in close single-file formation "lobster trains", sometimes for hundreds of miles.
Some bar-tailed godwits have the longest known non-stop flight of any migrant, flying 11,000 km from Alaska to their New Zealand non-breeding areas. Prior to migration, 55 percent of their bodyweight is stored fat to fuel this uninterrupted journey.
The Mediterranean and other seas present a major obstacle to soaring birds, which must cross at the narrowest points. Massive numbers of large raptors and storks pass through areas such as Gibraltar, Falsterbo, and the Bosphorus at migration times. More common species, such as the Honey Buzzard, can be counted in hundreds of thousands in autumn. Other barriers, such as mountain ranges, can also cause funnelling, particularly of large diurnal migrants. This is a notable factor in the Central American migratory bottleneck. This concentration of birds during migration can put species at risk. Some spectacular migrants have already gone extinct, the most notable being the Passenger Pigeon. During migration the flocks were a mile (1.6 km) wide and 300 miles (500 km) long, taking several days to pass and containing up to a billion birds.
- Main article: Shoaling and schooling
|30px External images|
The term "shoal" can be used to describe any group of fish, including mixed-species groups, while "school" is used for more closely knit groups of the same species swimming in a highly synchronised and polarised manner.
Fish derive many benefits from shoaling behaviour including defence against predators (through better predator detection and by diluting the chance of capture), enhanced foraging success, and higher success in finding a mate. It is also likely that fish benefit from shoal membership through increased hydrodynamic efficiency.
Fish use many traits to choose shoalmates. Generally they prefer larger shoals, shoalmates of their own species, shoalmates similar in size and appearance to themselves, healthy fish, and kin (when recognised). The "oddity effect" posits that any shoal member that stands out in appearance will be preferentially targeted by predators. This may explain why fish prefer to shoal with individuals that resemble them. The oddity effect would thus tend to homogenise shoals.
One puzzling aspect of shoal selection is how a fish can choose to join a shoal of animals similar to themselves, given that it cannot know its own appearance. Experiments with zebrafish have shown that shoal preference is a learned ability, not innate. A zebrafish tends to associate with shoals that resemble shoals in which it was reared, a form of imprinting.
Other open questions of shoaling behaviour include identifying which individuals are responsible for the direction of shoal movement. In the case of migratory movement, most members of a shoal seem to know where they are going. In the case of foraging behaviour, captive shoals of golden shiner (a kind of minnow) are led by a small number of experienced individuals who knew when and where food was available.
Radakov estimated herring schools in the North Atlantic can occupy up to 4.8 cubic kilometres with fish densities between 0.5 and 1.0 fish/cubic metre. That's several billion fish in one school.
- See also: Eel life history
- Partridge BL (1982) "The structure and function of fish schools" Scientific American, June:114–123.
- Parrish JK, Viscido SV, Grunbaum D (2002). Self-Organized Fish Schools: An Examination of Emergent Properties. Biol. Bull. 202 (3): 296–305.
- Main article: Fish migration
|30px External images|
|16px||Video clip of the "Sardine run"|
Between May and July huge numbers of sardines spawn in the cool waters of the Agulhas Bank and then follow a current of cold water northward along the east coast of South Africa. This great migration, called the sardine run, creates spectacular feeding frenzies along the coastline as marine predators, such as dolphins, sharks and gannets attack the schools.
Most krill, small shrimp-like crustaceans, form large swarms, sometimes reaching densities of 10,000–60,000 individual animals per cubic metre. Swarming is a defensive mechanism, confusing smaller predators that would like to pick out single individuals. Krill typically follow a diurnal vertical migration. Until recently it has been assumed that they spend the day at greater depths and rise during the night toward the surface. It has been found that the deeper they go, the more they reduce their activity, apparently to reduce encounters with predators and to conserve energy. Later work suggested that swimming activity in krill varied with stomach fullness. Satiated animals that had been feeding at the surface swim less actively and therefor sink below the mixed layer. As they sink they produce faeces which may mean that they have an important role to play in the Antarctic carbon cycle. Krill with empty stomachs were found to swim more actively and thus head towards the surface. This implies that vertical migration may be a bi- or tri-daily occurrence. Some species form surface swarms during the day for feeding and reproductive purposes even though such behaviour is dangerous because it makes them extremely vulnerable to predators. Dense swarms may elicit a feeding frenzy among fish, birds and mammal predators, especially near the surface. When disturbed, a swarm scatters, and some individuals have even been observed to moult instantaneously, leaving the exuvia behind as a decoy.
- See also: Hunting copepods
Copepods are a group of tiny crustaceans found in the sea and lakes. Many species are planktonic (drifting in sea waters), and others are benthic (living on the ocean floor). Copepods are typically Template:Convert/toTemplate:Convert/test/A long, with a teardrop shaped body and large antennae. Although like other crustaceans they have an armoured exoskeleton, they are so small that in most species this thin armour, and the entire body, is almost totally transparent. Copepods have a compound, median single eye, usually bright red, in the centre of the transparent head.
Copepods also swarm. For example, monospecific swarms have been observed regularly around coral reefs and sea grass, and in lakes. Swarms densities were about one million copepods per cubic metre. Typical swarms were one or two metres in diameter, but some exceeded 30 cubic metres. Copepods need visual contact to keep together, and they disperse at night.
Spring produces blooms of swarming phytoplankton which provide food for copepods. Planktonic copepods are usually the dominant members of the zooplankton, and are in turn major food organisms for many other marine animals. In particular, copepods are prey to forage fish and jellyfish, both of which can assemble in vast, million-strong swarms. Some copepods have extremely fast escape responses when a predator is sensed and can jump with high speed over a few millimetres (see animated image below).
Planktonic copepods are important to the carbon cycle. Some scientists say they form the largest animal biomass on earth. They compete for this title with Antarctic krill. Because of their smaller size and relatively faster growth rates, however, and because they are more evenly distributed throughout more of the world's oceans, copepods almost certainly contribute far more to the secondary productivity of the world's oceans, and to the global ocean carbon sink than krill, and perhaps more than all other groups of organisms together. The surface layers of the oceans are currently believed to be the world's largest carbon sink, absorbing about 2 billion tons of carbon a year, the equivalent to perhaps a third of human carbon emissions, thus reducing their impact. Many planktonic copepods feed near the surface at night, then sink into deeper water during the day to avoid visual predators. Their moulted exoskeletons, faecal pellets and respiration at depth all bring carbon to the deep sea.
Many single-celled organisms called phytoplankton live in oceans and lakes. When certain conditions are present, such as high nutrient or light levels, these organisms reproduce explosively. The resulting dense swarm of phytoplankton is called an algal bloom. Blooms can cover hundreds of square kilometres and are easily seen in satellite images. Individual phytoplankton rarely live more than a few days, but blooms can last weeks.
- Main article: Swarming motility
Swarming is also used to describe groupings of some kinds of bacteria such as myxobacteria. Myxobacteria swarm together in "wolf packs", actively moving using a process known as bacterial gliding and keeping together with the help of intercellular molecular [[signal trC. Allison and C. Hughes. Bacterial swarming: an example of procaryotic differentiation and multicellular behaviour. Sci. Progress, 75:403–422, 1991. ansduction|signals]].
- Allison C, Hughes C (1991). Bacterial swarming: an example of prokaryotic differentiation and multicellular behaviour. Science Progress 75 (298 Pt 3–4): 403–422.
- Parrish JK, Edelstein-Keshet L (1999). Complexity, pattern and evolutionary trade-offs in animal aggregation. Science 284 (5411): 99–101.
|30px External images|
|16px||Mexican wave simulation|
|16px||Rhythmic applause simulation|
A collection of people can also exhibit swarm behaviour, such as pedestrians or soldiers swarming the parapets. In Cologne, Germany, two biologists from the University of Leeds demonstrated flock like behaviour in humans. The group of people exhibited similar behavioural pattern to a flock, where if five percent of the flock changed direction the others would follow. If one person was designated as a predator and everyone else was to avoid him, the flock behaved very much like a school of fish. Understanding how humans interact in crowds is important if crowd management is to effectively avoid casualties at football grounds, music concerts and subway stations.
The mathematical modelling of flocking behaviour is a common technology, and has found uses in animation. Flocking simulations have been used in many films to generate crowds which move realistically. Tim Burton's Batman Returns was the first movie to make use of swarm technology for rendering, realistically depicting the movements of a group of bats using the boids system. 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.
An ant-based computer simulation using only six interaction rules has also been used to evaluate aircraft boarding behaviour.
Also, ants provided some guidance for Douglas A. Lawson, a systems analyst at Southwest Airlines. :"Because we know that ants have accomplished these amazing things right based on very simple rules, we know that if we want to see something complicated happen - like completely filling the interior of an aircraft with people - we know that simple ant-type behavior is adequate to represent what's occurring," Lawson said. "So Southwest Airlines said, 'Help us figure out the most efficient way to help us get our passengers on a plane,' and you said, 'I know - I'll use ants'?" asked Salie. "Yeah, right. Because they do complicated things with very simple rules," Lawson said. Lawson used mathematically-modeled ants to determine the most efficient way of boarding a plane, which turns out to be open seating. "So Southwest's way of boarding without seat numbers is actually more efficient than when I board another airline and know exactly what my seat is?" asked Salie. "Right. When we simulated what the different airlines are doing, it turns out that with assigned seats, there's a one-third chance that you're going to ask two people to get up, whereas open seating - since the middle seat is the undesirable one - generally that's the one that's last to be filled, [so] only one person is likely to get up, the person sitting near the aisle," said Lawson. "I may have to ask somebody to get up and get out of the way to let me get to a seat, and that's about it. So it's really simple. "So the ants are sort of an analogy - simple rules produce complicated transactions and events and major structures. and we can do the same thing in a little simulated world and represent very complicated processes," said Lawson.
Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. An airline system developed by Douglas A. Lawson uses swarm theory, or swarm intelligence – the idea that a colony of ants works better than one alone. 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," 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," says Lawson.
Swarm behaviour also occurs in traffic flow dynamics, such as the traffic wave. Bidirectional traffic can be observed in ant trails. In recent years this behaviour has been researched for insight into pedestrian and traffic models. Simulations based on pedestrian models have also been applied crowds which stampede because of panic.
Herd behaviour in marketing is used to explain the dependencies of customers' mutual behaviour. The Economist reported a recent conference in Rome on the subject of the simulation of adaptive human behaviour. It shared mechanisms to increase impulse buying and get people "to buy more by playing on the herd instinct." The basic idea is that people will buy more of products that are seen to be popular, and several feedback mechanisms to get product popularity information to consumers are mentioned, including smart card technology and the use of Radio Frequency Identification Tag technology. A "swarm-moves" model was introduced by a Florida Institute of Technology researcher, which is appealing to supermarkets because it can "increase sales without the need to give people discounts."
- Helbing D, Keltsch J, Molnar P (1997). Modelling the evolution of human trail systems. Nature 388 (6637): 47–50.
- Helbing D, Farkas I, Vicsek T (2000). Simulating dynamical features of escape panic. Nature 407 (6803): 487–490.
- Helbing D, Farkas IJ, Vicsek T (2000). Freezing by heating in a driven mesoscopic system. Physical review letters 84 (6): 1240–1243.
- Main article: Swarm robotics
- See also: Ant robotics
Inspired by colonies of insects such as ants and bees, researchers are modelling the behaviour of swarms of thousands of tiny robots which together perform a useful task, such as finding something hidden, cleaning, or spying. Each robot is quite simple, but the emergent behaviour of the swarm is more complex. The whole set of robots can be considered as one single distributed system, in the same way an ant colony can be considered a superorganism, exhibiting swarm intelligence. The largest swarms so far created include the iRobot swarm, the SRI/MobileRobots CentiBots project and the Open-source Micro-robotic Project swarm, which are being used to research collective behaviours. Swarms are also more resistant to failure. Whereas one large robot may fail and ruin a mission, a swarm can continue even if several robots fail. This could make them attractive for space exploration missions, where failure is normally extremely costly.
The I-Swarm project aims to develop a large scale swarm of up to 1,000 small microrobots, about 2 x 2 x 1 mm in size. The small size means that sensory and computational capabilities will be limited. This will be compensated for by the emergent swarm effects. The robots operate on solar power and can communicate between each other. The robots will differ in the type of sensors, manipulators and the amount of computational power. The swarm is expected to have many applications, including micro assembly, biological, medical or cleaning tasks.
- Main article: Swarming (military)
Military swarming is a behaviour where autonomous or partially autonomous units of action attack an enemy from several different directions and then regroup. Pulsing, where the units shift the point of attack, is also a part of military swarming. Military swarming involves the use of a decentralized force against an opponent, in a manner that emphasizes mobility, communication, unit autonomy and coordination or synchronization. Historically military forces used principles of swarming without really examining them explicitly, but now active research consciously examines military doctrines that draw ideas from swarming.
Merely because multiple units converge on a target, they are not necessarily swarming. Siege operations do not involve swarming, because there is no manoeuvre; there is convergence but on the besieged fortification. Nor do guerilla ambushes constitute swarms, because they are "hit-and-run". Even though the ambush may have several points of attack on the enemy, the guerillas withdraw when they either have inflicted adequate damage, or when they are endangered.
- There is a popular myth that lemmings commit mass suicide by swarming off cliffs when they migrate. Driven by strong biological urges, some species of lemmings may migrate in large groups when population density becomes too great. Lemmings can swim and may choose to cross a body of water in search of a new habitat. In such cases, many may drown if the body of water is so wide as to stretch their physical capability to the limit. This fact combined with an unexplained fluctuations in the population of Norwegian lemmings gave rise to the myth.
- Piranha have a reputation as fearless fish that swarm in ferocious and predatory packs. However, recent research, which started "with the premise that they school as a means of cooperative hunting", discovered that they were in fact rather fearful fish, like other fish, who schooled for protection from their predators, such as cormorants, caimans and dolphins. Piranhas are "basically like regular fish with large teeth".
- ↑ (1998). Alternating steady state in one-dimensional flocking. Journal of Physics A: Mathematical and General 32 (8): L99–L105.
- ↑ 2.0 2.1 2.2 Reynolds CW (1987). Flocks, herds and schools: A distributed behavioral model. Computer Graphics 21 (4): 25–34.
- ↑ 3.0 3.1 Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I, Lecomte V, Orlandi A, Parisi G, Procaccini A, Viale M, Zdravkovic V (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc. Natl. Acad. Sci. U.S.A. 105 (4): 1232–7.
- ↑ Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989)
- ↑ Dorigo M and Birattari M (2007) "Swarm intelligence" Scholarpedia, 2(9):1462, revision #40214. doi:10.4249/scholarpedia.1462
- ↑ Hierarchy of Life. URL accessed on 6 October 2009.
- ↑ Parunak, H. v D. (2003). "Making swarming happen" In Proc. of Conf. on Swarming and Network Enabled Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR), McLean, Virginia, USA, January 2003.
- ↑ 8.0 8.1 Li YX, Lukeman R, Edelstein-Keshet L et al. (2007). Minimal mechanisms for school formation in self-propelled particles. Physica D: Nonlinear Phenomena 237 (5): 699–720.
- ↑ Toner J and Tu Y (1995) "Long-range order in a two-dimensional xy model: how birds fly together" Physical Revue Letters, 75 (23)(1995), 4326–4329.
- ↑ Topaz C, Bertozzi A (2004). Swarming patterns in a two-dimensional kinematic model for biological groups. SIAM J Appl Math 65 (1): 152–174.
- ↑ Topaz C, Bertozzi A, Lewis M (2006). A nonlocal continuum model for biological aggregation. Bull Math Bio 68 (7): 1601–1623.
- ↑ Carrillo J, Fornasier M and Toscani G (2010) "Particle, kinetic, and hydrodynamic models of swarming" Modeling and Simulation in Science, Engineering and Technology, Part 3, 297–336. DOI:10.1007/978-0-8176-4946-3_12
- ↑ Swarmanoid simulation
- ↑ Ant colony optimization Retrieved 15 December 2010.
- ↑ A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.
- ↑ M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italie, 1992.
- ↑ Hölldobler & Wilson (1990), pp. 143–179
- ↑ (1999) Ant Algorithms for Discrete Optimization, Artificial Life, MIT Press.
- ↑ Self driven particle model Interactive simulations, 2005, University of Colorado. Retrieved 10 April 2011.
- ↑ 20.0 20.1 Vicsek T, Czirok A, Ben-Jacob E, Cohen I, Shochet O (1995). Novel type of phase transition in a system of self-driven particles. Physical review letters 75 (6): 1226–1229.
- ↑ Czirók A, Vicsek T (2006). Collective behavior of interacting self-propelled particles. Physica A 281: 17–29.
- ↑ Buhl J, Sumpter DJT, Couzin D, Hale JJ, Despland E, Miller ER, Simpson SJ et al. (2006). From disorder to order in marching locusts. Science 312 (5778): 1402–1406.
- ↑ Toner J, Tu Y, Ramaswamy S (2005). Hydrodynamics and phases of flocks. Annals of Physics 318.
- ↑ Bertin E, Droz M an Grégoire G (2009) "Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis" J. Phys. A, 42(44): paper 445001. DOI:10.1088/1751-8113/42/44/445001
- ↑ (1995) "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks IV: 1942–1948.
- ↑ (1997) "The particle swarm: social adaptation of knowledge". Proceedings of IEEE International Conference on Evolutionary Computation: 303–308.
- ↑ Hu X Particle swarm optimization: Tutorial. Retrieved 15 December 2010.
- ↑ (2001) Swarm Intelligence, Morgan Kaufmann.
- ↑ Poli, R. (2007). An analysis of publications on particle swarm optimisation applications. Technical Report CSM-469.
- ↑ Poli, R. (2008). Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications 2008: 1–10.
- ↑ Feare C (1984) The Starling, Oxford University Press. ISBN 978-0192177056.
- ↑ Partridge BL (1982). The structure and function of fish schools. Scientific American 246 (6): 114–123.
- ↑ Hubbard S, Babak P, Sigurdsson S, Magnusson K (2004). A model of the formation of fish schools and migrations of fish. Ecol. Model 174.
- ↑ Rauch E, Millonas M, Chialvo D (1995). Pattern formation and functionality in swarm models. Physics Letters A 207 (3–4): 185–193.
- ↑ Allison C, Hughes C (1991). Bacterial swarming: an example of prokaryotic differentiation and multicellular behaviour. Science Progress 75 (298 Pt 3–4): 403–422.
- ↑ Ben-Jacob E, Cohen I, Shochet O, Czirok A, Vicsek T (1995). Cooperative Formation of Chiral Patterns during Growth of Bacterial Colonies. Physical review letters 75 (15): 2899–2902.
- ↑ Rappel WJ, Nicol A, Sarkissian A, Levine H, Loomis WF (1999). Self-organized vortex state in two-dimensional Dictyostelium dynamics. Physical review letters 83 (6): 1247–1250.
- ↑ Chowdhury D (2006) "Collective effects in intra-cellular molecular motor transport: coordination, cooperation and competetion" Physica A, 372( 1): 84–95. DOI:10.1016/j.physa.2006.05.005
- ↑ Parrish JK and Hamner WM (eds) (1997) Animal Groups in Three Dimensions Cambridge University Press. ISBN 9780521460248.
- ↑ Helbing D, Keltsch J, Molnar P (1997). Modelling the evolution of human trail systems. Nature 388 (6637): 47–50.
- ↑ Helbing D, Farkas I, Vicsek T (2000). Simulating dynamical features of escape panic. Nature 407 (6803): 487–490.
- ↑ Helbing D, Farkas IJ, Vicsek T (2000). Freezing by heating in a driven mesoscopic system. Physical review letters 84 (6): 1240–1243.
- ↑ 43.0 43.1 Bonabeau E and Theraulaz G (2008) "Swarm Smarts". In Your Future with Robots Scientific American Special Editions.
- ↑ 44.0 44.1 Swarm Intelligence: An Interview with Eric Bonabeau, OpenP2P.com, 2003.
- ↑ Dicke E, Byde A, Cliff D, Layzell P (2004). Proceedings of Biologically Inspired Approaches to Advanced Information Technology: First International Workshop, BioADIT 2004 LNCS 3141: 364–379.
- ↑ Milius, Susan Swarm Savvy, How bees, ants and other animals avoid dumb collective decisions; Science News, May 9th, 2009; Vol.175 #10 (p. 16)
- ↑ Bee Swarms Follow High-speed 'Streaker' Bees To Find A New Nest; ScienceDaily (Nov. 24, 2008)
- ↑ Seeley, Thomas D.; Visscher, P. Kirk Choosing a home: how the scouts in a honey bee swarm perceive the completion of their group decision making Behavioral Ecology and Sociobiology (2003) 54:511–520 DOI 10.1007/s00265-003-0664-6
- ↑ Morse, R.A. Swarm orientation in honeybees, Science 141 (1963), pp. 357–358.
- ↑ Seeley, Thomas Consensus building during nest-site selection in honey bee swarms: The expiration of dissent Behavioral Ecology and Sociobiology Volume 53, Issue 6, 1 May 2003, Pages 417-424
- ↑ Jennifer Viegas. Cockroaches Make Group Decisions. Discovery Channel. URL accessed on 10 June 2006.
- ↑ Lemonick, Michael D. Robotic Roaches Do the Trick. Time Magazine.
- ↑ Locust Locustidae National Geographic. Retrieved 12 December 2010.
- ↑ BBC News | Locust swarms 'high' on serotonin
- ↑ Rogers SM, Matheson T, Despland E, Dodgson T, Burrows M, Simpson SJ (November 2003). Mechanosensory-induced behavioural gregarization in the desert locust Schistocerca gregaria. J. Exp. Biol. 206 (Pt 22): 3991–4002.
- ↑ Stevenson, PA (2009). The Key to Pandora's Box. Science 323 (5914): 594–5.
- ↑ Blocking 'happiness' chemical may prevent locust plagues, New scientist, 2009-01-29, accessed 2009-01-31
- ↑ Yates, CA (2009). Inherent noise can facilitate coherence in collective swarm motion. Proc. Natl. Acad. Sci. U.S.A. 106 (14): 5464–9.
- ↑ Pyle, Robert Michael, "National Audubon Society Field Guide to North American Butterflies", p712-713, Alfred A. Knopf, New York, ISBN 0-394-51914-0
- ↑ Monarch, Danaus plexippus. URL accessed on 2008-08-27.
- ↑ Gugliotta, Guy (2003): Butterflies Guided By Body Clocks, Sun Scientists Shine Light on Monarchs' Pilgrimage. Washington Post, May 23, 2003, page A03. Retrieved 2006-JAN-07.
- ↑ Merlin C, Gegear RJ, Reppert SM (2009). Antennal Circadian Clocks Coordinate Sun Compass Orientation in Migratory Monarch Butterflies. Science 325 (5948): 1700–1704.
- ↑ Kyriacou CP (2009). Unraveling Traveling. Science 325 (5948): 1629–1630.
- ↑ Sekercioglu, C.H. (2007). Conservation ecology: area trumps mobility in fragment bird extinctions. Current Biology 17 (8): R283–R286.
- ↑ Hummel D., Beukenberg M. (1989). Aerodynamische Interferenzeffekte beim Formationsfl ug von Vogeln. J. Ornithol 130: 15–24.
- ↑ Cutts, C. J. & J R Speakman (1994). Energy savings in formation flight of Pink-footed Geese. J. Exp. Biol. 189 (1): 251–261.
- ↑ Newton, I. (2008). The Migration Ecology of Birds, Elselvier.
- ↑ Fish, F.E. Kinematics of ducklings swimming in formation: consequences of position. (1995) Journal of Experimental Zoology 273:1-11.
- ↑ Alexander, R McNeill (2004). Hitching a lift hydrodynamically - in swimming, flying and cycling. Journal of Biology 3 (2).
- ↑ Drag Reduction from Formation Flight. Flying Aircraft in Bird-Like Formations Could Significantly Increase Range; Defense Technical Information Center; April 2002; Retrieved February 27, 2008
- ↑ (2008). Effects of Leaders Position and Shape on Aerodynamic Performances of V Flight Formation. Proceedings of the International Conference on Intelligent Unmanned System (ICIUS), Bali, Indonesia, October 24–25, Paper No. ICIUS2007-A008 0804: 3879.
- ↑ Gill, Robert E. Jr., Theunis Piersma, Gary Hufford, Rene Servranckx, Adrian Riegen (2005). Crossing the ultimate ecological barrier: evidence for an 11,000 km-long nonstop flight from Alaska to New Zealand and Eastern Australia by Bar-tailed Godwits. The Condor 107 (1): 1–20.
- ↑ Pitcher et al. 1982.
- ↑ Pitcher TJ and Parish JK (1993) "Functions of shoaling behaviour in teleosts" In: Pitcher TJ (ed) Behaviour of teleost fishes. Chapman and Hall, New York, pp 363–440
- ↑ Hoare DJ, Krause J, Peuhkuri N and Godin JGJ (2000) Body size and shoaling in fish Journal of Fish Biology, 57(6) 1351-1366.
- ↑ Snekser JL, Ruhl N, Bauer K, McRobert SP (2010). The Influence of Sex and Phenotype on Shoaling Decisions in Zebrafish. International Journal of Comparative Psychology 23: 70–81.
- ↑ Engeszer RE, Ryan MJ, Parichy DM (2004). Learned Social Preference in Zebrafish. Current Biology 14 (10): 881–884.
- ↑ Reebs, S.G. (2000). Can a minority of informed leaders determine the foraging movements of a fish shoal?. Animal Behaviour 59 (2): 403–9.
- ↑ Radakov DV (1973) Schooling in the ecology of fish. Israel Program for Scientific Translation, translated by Mill H. Halsted Press, New York. ISBN 9780706513516
- ↑ David Attenborough in The Blue Planet
- ↑ Hamner, WM (1983). Behavior of Antarctic Krill, Euphausia superba: Chemoreception, Feeding, Schooling and Molting'. Science 220 (4595): 433–5.
- ↑ U. Kils, P. Marshall (1995). "Der Krill, wie er schwimmt und frisst – neue Einsichten mit neuen Methoden ("The Antarctic krill – how it swims and feeds – new insights with new methods")" I. Hempel, G. Hempel Biologie der Polarmeere – Erlebnisse und Ergebnisse (Biology of the Polar Oceans Experiences and Results), 201–210, Fischer Verlag.
- ↑ R. Piper (2007). Extraordinary Animals: An Encyclopedia of Curious and Unusual Animals, Greenwood Press.
- ↑ J.S. Jaffe, M.D. Ohmann, A. de Robertis (1999). Sonar estimates of daytime activity levels of Euphausia pacifica in Saanich Inlet. Canadian Journal of Fisheries and Aquatic Sciences 56 (11): 2000–10.
- ↑ Geraint A. Tarling & Magnus L. Johnson (2006). Satiation gives krill that sinking feeling. Current Biology 16 (3): 83–4.
- ↑ Howard, D.: "Krill", pp. 133–140 in Karl, H.A. et al. (eds): Beyond the Golden Gate – Oceanography, Geology, Biology, and Environmental Issues in the Gulf of the Farallones, USGS Circular 1198, 2001. URLs last accessed 2010-06-04.
- ↑ D. Howard. Krill in Cordell Bank National Marine Sanctuary. NOAA. URL accessed on 15 June 2005.
- ↑ Hamner WM and Carleton JH (1979) "Copepod swarms: Attributes and role in coral reef ecosystems" Limnol. Oceanogr., 24(l):1-14.
- ↑ Johannes Dürbaum & Thorsten Künnemann. Biology of Copepods: An Introduction. Carl von Ossietzky University of Oldenburg. URL accessed on December 8, 2009.
- ↑ Lindsey R and Scott M (2010) What are phytoplankton NASA Earth Observatory.
- ↑ Harmful algal blooms in the Great Lakes 2009, NOAA, Center of Excellence for Great Lakes and Human Health.
- ↑ Reichenbach H (2001). Myxobacteria, producers of novel bioactive substances. J Ind Microbiol Biotechnol 27 (3): 149–56.
- ↑ Farkas I, Helbing D, Vicsek T (2002). Mexican waves in an excitable medium. Nature 419 (6903): 131–132.
- ↑ Neda Z, Ravasz E, Brechet Y, Vicsek T, Barabasi AL (2002). Physics of Rhythmic Applause. Physical Review E 61 (6): 6987–6992.
- ↑ Helbing D, Keltsch J and Molnar P (1997) "Modelling the evolution of human trail systems" Nature, 388:47–50. Template:Rxiv
- ↑ "http://psychcentral.com/news/2008/02/15/herd-mentality-explained/1922.html". Retrieved on October 31st 2008.
- ↑ http://www.thenational.ae/news/uae-news/science/danger-in-numbers-during-haj?pageCount=2
- ↑ Couzin ID, Krause J (2003). Self-organization and collective behavior in vertebrates. Advances in the Study of Behavior 32: 1–75.
- ↑ Gabbai, J.M.E. (2005). Complexity and the Aerospace Industry: Understanding Emergence by Relating Structure to Performance using Multi-Agent Systems.
- ↑ Livermore R (2008) "A multi-agent system approach to a simulation study comparing the performance of aircraft boarding using pre-assigned seating and free-for-all strategies" Open University, Technical report No 2008/25.
- ↑ Salie, Faith. 2011. "Small Wonders: What ants can teach us." CBS Sunday Morning. World Wide Web: http://www.cbsnews.com/2102-3445_162-20082643.html?tag=contentM Retrieved: July 25. 2011.
- ↑ "Planes, Trains and Ant Hills: Computer scientists simulate activity of ants to reduce airline delays" Science Daily, 1 April 2008.
- ↑ Burd, Martin, N. Aranwela (2003 February). Head-on encounter rates and walking speed of foragers in leaf-cutting ant traffic. Insectes Sociaux 50 (1): 3–8.
- ↑ Ribeiro, Pedro, André Frazão Helene, Gilberto Xavier, Carlos Navas, Fernando Leite Ribeiro (2009-04-01). Ants can learn to forage on one-way trails. PLoS ONE 4 (4): e5024.
- ↑ John, Alexander, Andreas Schadschneider, Debashish Chowdhury, Katsuhiro Nishinari (2008 March). Characteristics of ant-inspired traffic flow. Swarm Intelligence 2 (1): 25–41.
- ↑ Are we nearly there yet? Motorists could learn a thing or two from ants The Economist, 10 July 2009.
- ↑ (2000). Simulating dynamical features of escape panic. Nature 407 (6803): 487–490.
- ↑ includeonly>"Swarming the shelves: How shops can exploit people's herd mentality to increase sales?", The Economist, 2006-11-11, p. 90.
- ↑ ((cite web|http://www.activrobots.com/RESEARCH/wheelchair.html%7Ctitle=SRI/MobileRobots Centibot project))
- ↑ Open-source micro-robotic project. URL accessed on 2007-10-28.
- ↑ Swarm. iRobot Corporation. URL accessed on 2007-10-28.
- ↑ Knapp, Louise Look, Up in the Sky: Robofly. Wired Magazine. URL accessed on 2008-09-25.
- ↑ ISWARM, University of Karlsruhe, Research and Innovation in Baden-Württemberg http://www.wissenschaftwirtschaft-bw.de/index.php4?topic=universitaet&uni_id=6&profile_id=28
- ↑ 114.0 114.1 IPR Publications, 2004, Abstract http://wwwipr.ira.uka.de/en/Publications/detailed_publication.htm?id=1095254176
- ↑ 115.0 115.1 COGNIRON Links, I-SWARM, http://www.cogniron.org/Links.php
- ↑ Edwards, Sean J.A. (2000). Swarming on the Battlefield: Past, Present, and Future, Rand Corporation.
- ↑ Dive and Discover: Scientific Expedition 10: Antarctica. URL accessed on 2008-09-03.
- ↑ Crowd modelling: Simulating the behaviour of crowds of people, or swarms of animals, has both frivolous and important uses The Economist, 5 March 2009.
- ↑ Fisher, Len (2009) The perfect swarm: the science of complexity in everyday life Page 57. Basic Books. ISBN 978-0-465-01884-0
- ↑ Lemming Suicide Myth Disney Film Faked Bogus Behavior
- ↑ Red-Bellied Piranha Is Really Yellow New York Times, 24 May 2005.
- Blum C and Merkle D (2008) Swarm intelligence: introduction and applications Springer. ISBN 9783540740889.
- Camazine S, Deneubourg JL, Franks NR, Sneyd J, Theraulaz G and Bonabeau E (2003) Self-Organization in Biological Systems Princeton University Press. ISBN 9780691116242.
- Fisher L (2009) The perfect swarm: the science of complexity in everyday life Basic Books. ISBN 9780465018840.
- Kennedy JF, Kennedy J, Eberhart RC and Shi Y (2001) Swarm intelligence Morgan Kaufmann. ISBN 9781558605954.
- Krause, J (2005) Living in Groups Oxford University Press. ISBN 9780198508182
- Lim CP, Jain LC and Dehuri S (2009) Innovations in Swarm Intelligence Springer. ISBN 9783642042249.
- Miller, Peter (2010) The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision making, and getting things done Penguin, ISBN 978-1-58333-390-7
- Nedjah N and Mourelle LdM (2006) Swarm intelligent systems Springer. ISBN 9783540338680.
- New York times article on investigations into swarming
- From the Wolfram Demonstrations Project — requires CDF player (free):
|This page uses Creative Commons Licensed content from Wikipedia (view authors).|