An ensemble of intelligent water drop algorithms and its application to optimization problems

Alijla, Basem O., Wong, Li-Pei, Lim, Chee Peng, Khader, Ahamad Tajudin and Al-Betar, Mohammed Azmi 2015, An ensemble of intelligent water drop algorithms and its application to optimization problems, Information sciences, vol. 325, pp. 175-189, doi: 10.1016/j.ins.2015.07.023.

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Title An ensemble of intelligent water drop algorithms and its application to optimization problems
Author(s) Alijla, Basem O.
Wong, Li-Pei
Lim, Chee PengORCID iD for Lim, Chee Peng
Khader, Ahamad Tajudin
Al-Betar, Mohammed Azmi
Journal name Information sciences
Volume number 325
Start page 175
End page 189
Total pages 15
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-12
ISSN 0020-0255
Summary Crown Copyright © 2015 Published by Elsevier Inc. All rights reserved. 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 propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.
Language eng
DOI 10.1016/j.ins.2015.07.023
Field of Research 01 Mathematical Sciences
08 Information And Computing Sciences
09 Engineering
080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
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