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Swarm behaviouror swarmingis a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic. The term flocking or 'murmuration' are usually used to refer looking for auto trading robot api workers or worksheets to swarm behaviour in looking for auto trading robot api workers or worksheets, herding to refer to swarm behaviour in tetrapodsand shoaling or schooling to refer to swarm behaviour in fish.
Phytoplankton also gather in huge swarms called bloomsalthough these organisms are algae and are not self-propelled the way animals are.
From a more abstract point of view, swarm behaviour is the collective motion of a large number of self-propelled entities. Swarm behaviour is also studied by active matter physicists as a phenomenon which is not in thermodynamic equilibriumand as such requires the development of tools beyond those available from the statistical physics of systems in thermodynamic equilibrium. Swarm behaviour was first simulated on a computer in with the simulation program boids. 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.
In recent decades, scientists have turned to modeling swarm behaviour to gain a deeper understanding of the behaviour. Early studies of swarm behaviour employed mathematical models to simulate and understand the behaviour. The simplest mathematical models of animal swarms generally represent individual animals as following three rules:.
The boids computer program, created by Craig Reynolds insimulates swarm behaviour following the above rules. In the zone of repulsionvery 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 alignmentthe focal animal will seek to align its direction of motion with its neighbours.
In the outermost zone of attractionwhich 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 linewhile 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. It remains to be seen whether this applies to other animals. Another recent study, based on an analysis of high speed camera footage of flocks above Rome and assuming minimal behavioural rules, has convincingly simulated a number of aspects of flock behaviour.
In order to gain insight into why animals evolve swarming behaviour, scientists have turned to evolutionary models that simulate populations of evolving animals. Typically these studies use a genetic algorithm to simulate evolution over many generations in the model. These studies have investigated a number of hypotheses explaining why animals evolve swarming behaviour, such as the selfish herd theory [9] [10] [11] [12] the predator confusion effect, [13] [14] the dilution effect, [15] [16] and the many eyes theory.
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.
The queen does not give direct orders and does not tell the ants what to do. Instead, each ant reacts to looking for auto trading robot api workers or worksheets in the form of chemical scent from larvae, other ants, intruders, food and buildup 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.
A further key concept in the field of swarm intelligence is stigmergy. 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 intelligence is the collective behaviour of decentralizedself-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang inin the context of cellular robotic systems.
Swarm intelligence systems are typically made up of a population of simple agents such as 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. Looking for auto trading robot api workers or worksheets 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.
Swarm algorithms follow a Lagrangian approach or an Eulerian approach. It is a hydrodynamic approach, and can be useful for modelling the overall dynamics of large swarms. Individual particle models can follow information on heading and spacing that is lost in the Eulerian approach.
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. 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.
Self-propelled particles SPP is a concept introduced in by Vicsek et al. 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. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours. Particle swarm optimization is another algorithm widely used to solve problems related to swarms. It was developed in by Kennedy and Eberhart and was first aimed at simulating the social behaviour and choreography of bird flocks and fish schools.
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 looking for auto trading robot api workers or worksheets 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. Researchers in Switzerland have developed an algorithm based on Hamilton's rule of kin selection. The algorithm shows how altruism in a swarm of entities can, over time, evolve and result in more effective swarm behaviour. Examples of biological swarming are found in bird flocks[48] fish schools[49] [50] insect swarms[51] bacteria swarms[52] [53] molds, [54] molecular motors[55] quadruped herds [56] and people.
The behaviour of insects that live in coloniessuch as ants, bees, wasps and termites, has always been a source of fascination for children, naturalists and artists.
Individual insects seem to do their own thing without any central control, yet the colony as a whole behaves in a highly coordinated manner.
The group coordination that emerges is often looking for auto trading robot api workers or worksheets a consequence of the way individuals in the colony interact. These interactions can be remarkably looking for auto trading robot api workers or worksheets, 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 collectively select i. Selection of the best food source is achieved by ants following two simple rules. First, ants which find food return to the nest depositing a pheromone chemical. More pheromone is laid for higher quality food sources.
Ants in the nest follow another simple rule, to favor stronger trails, on average. More ants then follow the stronger trail, so more ants arrive at the high quality food source, and a positive feedback cycle ensures, resulting in a collective decision for the best food source. If there are two paths from the ant nest to a food source, then the colony usually selects the shorter path. This is because 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. The successful techniques used by ant colonies have been studied in computer science and robotics to produce distributed looking for auto trading robot api workers or worksheets 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. 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.
In this new location, the bees cluster about the queen and send 20 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 has 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 looking for auto trading robot api workers or worksheets 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 looking for auto trading robot api workers or worksheets, 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. Sometimes, if no decision is reached, looking for auto trading robot api workers or worksheets swarm will separate, some bees going in one direction; others, going in another. This usually results in failure, with both groups dying. A swarm may fly for a kilometre or more to the scouted out location, though some species may establish new colonies within as little as meters from the natal nest, such as Apis dorsata.
