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Swarm is an open-source agent-based modeling simulation package, useful for simulating the interaction of agents (social or biological) and their emergent collective behavior. Swarm was initially developed at the Santa Fe Institute in the mid-1990s, and since 1999 has been maintained by the non-profit Swarm Development Group .
Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence. The application of swarm principles to robots is called swarm robotics while swarm intelligence refers to the more general set of algorithms.
The scout bees are translated from a few employed bees, which abandon their food sources and search new ones. In the ABC algorithm, the first half of the swarm consists of employed bees, and the second half constitutes the onlooker bees. The number of employed bees or the onlooker bees is equal to the number of solutions in the swarm.
Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness). The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number T of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures ...
A population (swarm) of candidate solutions (particles) moves in the search space, and the movement of the particles is influenced both by their own best known position and swarm's global best known position. Like genetic algorithms, the PSO method depends on information sharing among population members.
In the 19th century, changes in beekeeping practice were completed through the development of the movable comb hive by the American Lorenzo Lorraine Langstroth, who was the first person to make practical use of Huber's earlier discovery of a specific spatial distance between the wax combs, later called the bee space, which bees do not block ...
Combinations of artificial ants and local search algorithms have become a preferred method for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing. As an example, ant colony optimization [ 3 ] is a class of optimization algorithms modeled on the actions of an ant colony . [ 4 ]
Environmental DNA or eDNA describes the genetic material present in environmental samples such as sediment, water, and air, including whole cells, extracellular DNA and potentially whole organisms. [13] [14] The analysis of eDNA starts with capturing an environmental sample of interest. The DNA in the sample is then extracted and purified.