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A bi-population QUasi-Affine TRansformation Evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks

Authors
  • Liu, Nengxian1
  • Pan, Jeng-Shyang1, 2, 3
  • Nguyen, Trong-The2, 4
  • 1 Fuzhou University, College of Mathematics and Computer Science, Fuzhou, 350108, China , Fuzhou (China)
  • 2 Fujian University of Technology, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou, 350118, China , Fuzhou (China)
  • 3 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266510, China , Qingdao (China)
  • 4 University of Manage and Technology, Department of Information Technology, Haiphong, 180000, Vietnam , Haiphong (Vietnam)
Type
Published Article
Journal
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer International Publishing
Publication Date
Jul 05, 2019
Volume
2019
Issue
1
Identifiers
DOI: 10.1186/s13638-019-1481-6
Source
Springer Nature
Keywords
License
Green

Abstract

In this paper, we propose a new Bi-Population QUasi-Affine TRansformation Evolution (BP-QUATRE) algorithm for global optimization. The proposed BP-QUATRE algorithm divides the population into two subpopulations with sort strategy, and each subpopulation adopts a different mutation strategy to keep the balance between the fast convergence and population diversity. What is more, the proposed BP-QUATRE algorithm dynamically adjusts scale factor with a linear decrease strategy to make a good balance between exploration and exploitation capability. We compare the proposed algorithm with two QUATRE variants, PSO-IW, and DE algorithms on the CEC2013 test suite. The experimental results demonstrate that the proposed BP-QUATRE algorithm outperforms the competing algorithms. We also apply the proposed algorithm to dynamic deployment in wireless sensor networks. The simulation results show that the proposed BP-QUATRE algorithm has better coverage rate than the other competing algorithms.

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