.png){kind=icon}
.png){kind=icon}
Return
An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems
.png){kind=icon}
PRE
AI
DOI：10.1080/09540091.2017.1379949.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Dynamic optimisation problems (DOPs) have attracted a lot of research attention in recent years due to their practical applications and complexity. DOPs are more challenging than static optimisation problems because the problem information or data is either revealed or changed during the course of an ongoing optimisation process. This requires an optimisation algorithm that should be able to monitor the movement of the optimal point and the changes in the landscape solutions. In this paper, we proposed an Interleaved Artificial Bee Colony (I-ABC) algorithm for DOPs. Artificial Bee Colony (ABC) is a nature inspired algorithm which has been successfully used in various optimisation problems. The proposed I-ABC algorithm has two populations, called ABC1 and ABC2, which worked in an interleaved manner. While ABC1 focused on exploring the search space though using a probabilistic solution acceptance mechanism, ABC2 worked inside ABC1 and focused on the search around the current best solutions by using a greedy mechanism. The proposed algorithm was tested on the Moving Peak Benchmark. The experimental results indicated that the proposed algorithm achieved better results than the compared methods for 8 out of 11 scenarios.
Keywords:
Dynamic optimisation
Interleaved Artificial Bee Colony algorithm
Moving Peak Benchmark problem
probabilistic acceptance
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
843
Citations:
1.5K