Home > Archive > 2021 > Volume 11 Number 5 (Sept. 2021) >
IJMLC 2021 Vol.11(5): 327-332 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.5.1056

Optimized Artificial Bee Colony Algorithm for Web Service Composition Problem

Shudong Zhang, Yaru Shao, and Lijuan Zhou

Abstract—With the proliferation of web services offering similar functionality, how to choose the optimal service composition that meet consumer demands based on Qos-aware has become increasingly difficult. In this paper, we study the web service composition problem in a sequential composition model. In this work, we propose an optimized artificial bee colony algorithm (OABC) to make it more suitable for the problem. On one hand, this algorithm uses the group initialization strategy based on opposition learning to enhance the diversity of the initial population. On the other hand, in order to improve the exploration and exploitation ability of the algorithm, we dynamically adjust the neighborhood search range during the hired bee phase and introduce a global optimal bee behavior in the scout bee phase. Our experimental results demonstrate the good performance of the solutions obtained by OABC algorithm in fitness and execution time when compared with other existing algorithms.

Index Terms—Web service composition, Qos-aware, artificial bee colony (ABC), optimized artificial bee colony (OABC).

Shudong Zhang, Yaru Shao, and Lijuan Zhou are with the College of Information Engineering, Capital Normal University, Beijing, 100089, China (Corresponding author: Lijuan Zhou; e-mail: zhangshudong@cnu.edu.cn, 2181002051@cnu.edu.cn, zhoulijuan@cnu.edu.cn).


Cite: Shudong Zhang, Yaru Shao, and Lijuan Zhou, "Optimized Artificial Bee Colony Algorithm for Web Service Composition Problem," International Journal of Machine Learning and Computing vol. 11, no. 5, pp. 327-332, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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