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

被引：5310

作者：

Karaboga, Dervis ^{[1
]}

Basturk, Bahriye ^{[1
]}

机构：

[1] Erciyes Univ, Dept Comp Engn, Kayseri, Turkey

来源：

JOURNAL OF GLOBAL OPTIMIZATION | 2007年 / 39卷 / 03期

关键词：

swarm intelligence; artificial bee colony; particle swarm optimization; genetic algorithm; particle swarm inspired evolutionary algorithm; numerical function optimization;

D O I：

10.1007/s10898-007-9149-x

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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.

引用

页码：459 / 471

页数：13

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