.png){kind=icon}
返回
A comparative study of Artificial Bee Colony algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2009.03.090.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. (C) 2009 Elsevier Inc. All rights reserved.
Keyword:
Swarm intelligence
Evolution strategies
Genetic algorithms
Differential evolution
Particle swarm optimization
Artificial Bee Colony algorithm
Unconstrained optimization
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
Post‐stroke depression and functional recovery in a population‐based stroke register. The Finnstroke study基于人群的卒中登记中的卒中后抑郁和功能恢复。Finnstroke研究
.png){kind=icon}
PREAI
分享收藏
A new design method based on artificial bee colony algorithm for digital IIR filters基于人工蜂群算法的数字IIR滤波器设计新方法
PREAI
分享收藏分享收藏
查看更多内容