Title: Application of many-objective particle swarm algorithm based on fitness allocation in WSN coverage optimisation

Authors: Weiwei Yu; Chengwang Xie

Addresses: School of Computer Science and Engineering, Beihang University, Beijing 100191, China ' School of Computer and Information Engineering, Nanning Normal University, Nanning 530299, Guangxi, China

Abstract: In order to improve the situation that the Wireless Sensor Network (WSN) nodes in the random deployment are not uniform and improve the network coverage performance, many-objective Particle Swarm Algorithm based on Fitness Allocation (FAMPSO) is proposed. The algorithm combines the fuzzy information theory to associate the Ideal Solution (IS) with the Pareto Solution (PS) and proposes a new fitness allocation method, which increases the pressure of population selection and enhances the convergence of the algorithm. The FAMPSO algorithm is compared with three other representative multi-objective evolution algorithms on the DTLZ series test function set. At the same time, the FAMPSO algorithm was applied to the coverage optimisation of WSN, and the simulation analysis was carried out. The simulation results show that the FAMPSO algorithm has a significant performance advantage in terms of convergence, diversity and robustness. FAMPSO algorithm improves the coverage performance of WSN.

Keywords: WSN; wireless sensor network; network coverage; particle swarm optimisation; many-objective optimisation; fitness allocation.

DOI: 10.1504/IJWMC.2021.115642

International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.3, pp.255 - 263

Received: 06 Aug 2020
Accepted: 14 Sep 2020
Published online: 15 Jun 2021 *

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