.png){kind=icon}
.png){kind=icon}
Return
A quick artificial bee colony (qABC) algorithm and its performance on optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2014.06.035.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms' performances. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Optimization
Swarm intelligence
Artificial bee colony
Quick artificial bee colony
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.