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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.
Pheromone-based communication is one of the most effective ways of communication which is widely observed in nature. Pheromone is used by social insects such as bees, ants and termites; both for inter-agent and agent-swarm communications. Due to its feasibility, artificial pheromones have been adopted in multi-robot and swarm robotic systems.
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 ...
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 ...
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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 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.
Differential Evolution optimizing the 2D Ackley function.. Differential evolution (DE) is an evolutionary algorithm to optimize a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.