Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | Connection Science |
| DOIs | |
| Publication status | Accepted/In press - 28 Sep 2017 |
Keywords
- Dynamic optimisation
- Interleaved Artificial Bee Colony algorithm
- Moving Peak Benchmark problem
- probabilistic acceptance
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Artificial Intelligence
Cite this
An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems. / Abdullah, Salwani; Nseef, Shams K.; Turky, Ayad.
In: Connection Science, 28.09.2017, p. 1-13.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems
AU - Abdullah, Salwani
AU - Nseef, Shams K.
AU - Turky, Ayad
PY - 2017/9/28
Y1 - 2017/9/28
N2 - 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.
AB - 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.
KW - Dynamic optimisation
KW - Interleaved Artificial Bee Colony algorithm
KW - Moving Peak Benchmark problem
KW - probabilistic acceptance
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U2 - 10.1080/09540091.2017.1379949
DO - 10.1080/09540091.2017.1379949
M3 - Article
SP - 1
EP - 13
JO - Connection Science
JF - Connection Science
SN - 0954-0091
ER -