The artificial bee colony algorithm is a population-based metaheuristic optimization algorithm that models how a honey bee colony forages for food sources. It divides its population into three roles, employed bees that each exploit one known food source and share information about it through a waggle-dance style communication step, onlooker bees that choose which advertised food source to exploit based on how good the reported sources are, and scout bees that abandon a source once it has stopped improving and go searching for a new one at random; in the algorithm each food source stands for a candidate solution and its nectar amount stands for that solution's fitness, so the colony's collective foraging behavior is used to steer the population toward better solutions over successive cycles. Dervis Karaboga of Erciyes University proposed the algorithm in 2005, and it has since been applied to a range of practical optimization problems in computer science and operations research.
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Design TechniqueHeuristic or Approximation 1 Connections
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Source Artificial Bee Colony Algorithm (Wikipedia)
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1. Artificial Bee Colony Algorithm (Wikipedia)
Wikipedia: design technique heuristic-approximation
heuristic-approximation
In Field: Evolutionary Computation, Lead section
In computer science and operations research, the artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm, proposed by Derviş Karaboğa (Erciyes University) in 2005.
In Group: Metaheuristic Optimization Algorithms, Lead section
is an optimization algorithm based on the intelligent foraging
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