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Journal of Sensors
Volume 2018, Article ID 4839090, 8 pages
Research Article

An Improved Niche Chaotic Genetic Algorithm for Low-Energy Clustering Problem in Large-Scale Wireless Sensor Networks

1College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang 832003, China
2The Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Group, Shihezi University, Shihezi, Xinjiang 832003, China

Correspondence should be addressed to Xin Lv; moc.621@zhsxl

Received 4 October 2017; Revised 13 January 2018; Accepted 6 February 2018; Published 1 April 2018

Academic Editor: Jaime Lloret

Copyright © 2018 Min Tian et al. 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.


Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.