An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems

Abdullah, S, Nseef, S and Turky, A 2018, 'An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems', Connection Science, vol. 30, no. 3, pp. 272-284.

Document type: Journal Article
Collection: Journal Articles

Title An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems
Author(s) Abdullah, S
Nseef, S
Turky, A
Year 2018
Journal name Connection Science
Volume number 30
Issue number 3
Start page 272
End page 284
Total pages 13
Publisher Taylor and Francis Ltd
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.
Subject Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) Dynamic optimisation
Interleaved Artificial Bee Colony algorithm
Moving Peak Benchmark problem
probabilistic acceptance
DOI - identifier 10.1080/09540091.2017.1379949
Copyright notice © 2017 Informa UK Limited, trading as Taylor & Francis Group.
ISSN 0954-0091
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 6 Abstract Views  -  Detailed Statistics
Created: Thu, 23 May 2019, 08:44:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us