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Artificial Bee Colony Algorithm

Optimization Algorithm

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.

Facts
Classification
Design Technique
Heuristic or Approximation 1
Connections

In Field

Source Artificial Bee Colony Algorithm (Wikipedia)

Uses Design Technique

Sources
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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