A modified Intelligent Water Drops algorithm and its application to optimization problems

Alijla, BO, Wong, L-P, Lim, Chee Peng, Khader, AT and Al-Betar, MA 2014, A modified Intelligent Water Drops algorithm and its application to optimization problems, Expert Systems with Applications, vol. 41, no. 15, pp. 6555-6569, doi: 10.1016/j.eswa.2014.05.010.

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Title A modified Intelligent Water Drops algorithm and its application to optimization problems
Author(s) Alijla, BO
Wong, L-P
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Khader, AT
Al-Betar, MA
Journal name Expert Systems with Applications
Volume number 41
Issue number 15
Start page 6555
End page 6569
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-11-01
ISSN 0957-4174
Keyword(s) Feature selection (FS)
Intelligent Water Drops (IWD)
Multiple knapsack problem (MKP)
Ranking-based selection methods
Rough set (RS)
Swarm-based optimization
Travelling salesman problem (TSP)
Summary The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. © 2014 Elsevier Ltd. All rights reserved.
Language eng
DOI 10.1016/j.eswa.2014.05.010
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
01 Mathematical Sciences
08 Information And Computing Sciences
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070151

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
2018 ERA Submission
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