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Swarming (honey bee) Swarming is a honey bee colony's natural means of reproduction. In the process of swarming, a single colony splits into two or more distinct colonies. [1] Swarming is mainly a spring phenomenon, usually within a two- or three-week period depending on the locale, but occasional swarms can happen throughout the producing season.
Artificial bee colony (ABC) algorithm is an optimization technique that simulates the foraging behavior of honey bees, and has been successfully applied to various practical problems [citation needed]. ABC belongs to the group of swarm intelligence algorithms and was proposed by Karaboga in 2005. A set of honey bees, called swarm, can ...
Swarm intelligence. Swarm intelligence ( SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. [1]
Swarm is the name of an open-source agent-based modeling simulation package, useful for simulating the interaction of agents (social or biological) and their emergent collective behaviour. 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 .
Swarm robotic platforms apply swarm robotics in multi-robot collaboration. They take inspiration from nature (e.g. collective problem solving mechanisms seen in nature such as honey bee aggregation). The main goal is to control a large number of robots (with limited sensing/processing ability) to accomplish a common task/problem.
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Bees algorithm. In computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. [1] It mimics the food foraging behaviour of honey bee colonies.
Parity benchmark. v. t. e. In computational science, particle swarm optimization ( PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these ...