Title: Behaviour-based grey wolf optimiser for a wireless sensor network deployment problem
Authors: Yu Qiao; Hung-Yao Hsu; Jeng-Shyang Pan
Addresses: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China ' UniSA STEM, University of South Australia, Mawson Lakes Blvd, Mawson Lakes, SA 5095, Australia ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Abstract: The existing grey wolf optimiser does not perform well in convergence and diversity of the population. This paper investigates the grey wolf optimiser and proposes a behaviour-based grey wolf optimiser (BGWO) based on the real behaviours of the wolf pack. In BGWO, it mainly consists of two strategies: The lost wolf strategy and the mating strategy. BGWO increases the population diversity of wolves so that it is not easy to fall into a local optimum. Eighteen benchmark functions in CEC2017 are used to test the performance of BGWO and the result shows that the performance of BGWO is better than existing algorithms in the literature such as GWO, PSO, FA, and PSOGWO. Moreover, In the WSN problem, a combination of coverage rate, connectivity rate, and the total network energy consumption is proposed as the objective function and optimised by BGWO. The experimental results show that BGWO performs well than other algorithms.
Keywords: behaviour-based grey wolf optimiser; BGWO; lost wolf strategy; mating strategy; WSN deployment.
International Journal of Ad Hoc and Ubiquitous Computing, 2022 Vol.39 No.1/2, pp.70 - 82
Received: 18 Oct 2020
Accepted: 09 Dec 2020
Published online: 18 Feb 2022 *