Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

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A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.

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On the performance of artificial bee colony (ABC) algorithm

A comparative study of Artificial Bee Colony algorithm

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

http://romisatriawahono.net/lecture/rm/survey/softcomputing/Boussaid%20-%20Optimization%20Metaheuristics%20-%202013.pdf

A survey on optimization metaheuristics

Bees organize their foraging activities as a social and communicative effort, indicating both the direction, distance and quality of food sources to their fellow foragers through a ”dance” inside the bee hive (on the ”dance floor”). In this paper we present a novel routing algorithm, BeeHive, which has been inspired by the communicative and evaluative methods and procedures of honey bees. In this algorithm, bee agents travel through network regions called foraging zones. On their way their information on the network state is delivered for updating the local routing tables. BeeHive is fault tolerant, scalable, and relies completely on local, or regional, information, respectively. We demonstrate through extensive simulations that BeeHive achieves a similar or better performance compared to state-of-the-art algorithms.

Yue Zhang

Papers

BeeHive: An efficient fault-tolerant routing algorithm inspired by honey bee behavior