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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 .
Particle swarm optimization (PSO) A swarm intelligence method. Intelligent water drops (IWD) A swarm-based optimization algorithm based on natural water drops flowing in rivers Gravitational search algorithm (GSA) A swarm intelligence method. Ant colony clustering method (ACCM) A method that make use of clustering approach, extending the ACO.
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 following requirements apply to all mutation operators used in an EA: [2] [3] every point in the search space must be reachable by one or more mutations. there must be no preference for parts or directions in the search space (no drift). small mutations should be more probable than large ones.
A synthetically produced Nasonov pheromone can be used to attract a honey bee swarm to an unoccupied hive or a swarm-catching box. Synthetically produced Nasonov consists of citral and geraniol in a 2:1 ratio. The Nasonov gland was first described in 1882 by the Russian zoologist Nikolai Viktorovich Nasonov.
The permissive temperature is the temperature at which a temperature-sensitive mutant gene product takes on a normal, functional phenotype. [2] When a temperature-sensitive mutant is grown in a permissive condition, the mutant gene product behaves normally (meaning that the phenotype is not observed), even if there is a mutant allele present.
A very small mutation rate may lead to genetic drift (which is non-ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless elitist selection is employed. An adequate population size ensures sufficient genetic ...
Demaree also described a swarm prevention method in 1884, but that was a two-hive system that is unrelated to modern "demareeing". [2] As with many swarm prevention methods, demareeing involves separating of the queen and forager bees from the nurse bees. The theory is that forager bees will think that the hive has swarmed if there is a drastic ...