.png){kind=icon}
.png){kind=icon}
Return
Artificial bee colony algorithm based on Parzen window method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2018.10.024.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, based on the metaphor in foraging behavior of honey fee swarm, has been repeatedly criticized for its poor convergence, due to its known exploration bias. In order to enhance the performance of ABC, the paper develops a novel approach (named ABCPW). First, three popular search strategies with different characteristics are employed to construct a strategy candidate pool for obtaining high quality candidate individuals. Next, to cut down on computational cost, the Parzen window method is applied to estimate these candidate individuals and then select one as the offspring. In addition, two different neighborhood mechanisms are adopted to balance the convergence and the population diversity. Finally, the performance of ABCPW is tested on a series of benchmark functions. The experimental results not only demonstrate the stability and convergence of ABCPW, but also show ABCPW outperforms several popular algorithms. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Parzen window method
Artificial bee colony algorithm
Strategy candidate pool
Neighborhood mechanism
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.