Swarm behaviour - Wikipedia. A flock of auklets exhibit swarm behaviour. Swarm behaviour, or swarming, is 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 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. Phytoplankton also gather in huge swarms called blooms, although these organisms are algae and are not self- propelled the way animals are. By extension, the term swarm is applied also to inanimate entities which exhibit parallel behaviours, as in a robot swarm, an earthquake swarm, or a swarm of stars. 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 equilibrium, and 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 1. 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. Mathematical models. In the topological distance model (right), the focal fish only pays attention to the six or seven closest fish (green), regardless of their distance. Murmuration is an integrated performance company based in Sydney, Australia. Murmuration is an unparalleled live music and dance event in Colorado. It pairs musicians and dancers in unexpected combinations to celebrate the community’s wide array of talent, creativity, and diversity. Middle English murmuracioun, from Middle French murmuration, from Latin murmuration- murmuratio, from murmuratus (past participle of murmurare) + -ion- -io-ion. The latest Tweets from Murmuration (@MurmurationFest). 3-day festival @CortexSTL featuring influential speakers, national #innovation thought-leaders, a juried #art competition, and cutting-edge #music performances. Murmuration Brendan Duke 7th, 7f 2yo maiden, Curragh, October 11 This stable had a couple of good performers in maidens during the week and this filly was probably the pick of them. 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: 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 1. 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. Www.islandsandrivers.com Facebook: Islands And Rivers Follow us @Islands Murmuration has 240 ratings and 93 reviews. In nature, a murmuration is a flock of starlings that produces intricate patterns during flight. The Murmuration Festival is a 3-day event that explores the intersection of art, music, science, and tech. We invite innovators. Murmurations: Spectacular Starlings Signal Winter Is On Its Way. Sonia van Gilder Cooke; Nov. Birds create phenomenal flocking and beautiful patterns in the sky at dusk. No one knows why they do it. How Do Starling Flocks Create Those Mesmerizing Murmurations? By Andrea Alfano, a Cornell University junior February 21, 2013. 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. 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. 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. 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 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. 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. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1. 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. 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. 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 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 . 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. It was developed in 1. 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 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. The algorithm shows how altruism in a swarm of entities can, over time, evolve and result in more effective swarm behaviour. 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 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. 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. This area of biomimetics has led to studies of ant locomotion, search engines that make use of . 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 2. 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 findings the more excitedly she dances.
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