A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
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DOI：
10.1007/s10898-007-9149-x
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发表时间：
2007-11-01
影响因子：
1.8
通讯作者：
Basturk, Bahriye
中科院分区：
文献类型：
--
作者：
Karaboga, Dervis;Basturk, Bahriye
群体智能是一个研究分支，它对能够自组织的相互作用的主体或群体进行建模。蚁群、鸟群或免疫系统是群系统的典型例子。蜜蜂在蜂箱周围蜂拥而至是群体智能的另一个例子。人工蜂群算法是一种基于蜂群智能行为的优化算法。将ABC算法用于多变量函数的优化，并对ABC算法、遗传算法(GA)、粒子群算法(PSO)和粒子群启发进化算法(PS-EA)的优化结果进行了比较。实验结果表明，ABC算法的性能优于其他算法。
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.