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Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

Xu, Ying; Ding, Ou; Qu, Rong; Li, Keqin

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Authors

Ying Xu

Ou Ding

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RONG QU rong.qu@nottingham.ac.uk
Professor of Computer Science

Keqin Li



Abstract

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network life-time and coverage. These include minimizing the energy consumption, maximizing the coverage rate and maximizing the equilibrium of energy consumption. Two improved hybrid multi-objective evolutionary algorithms, namely Hybrid-MOEA/D-I and Hybrid-MOEA/D-II, have been proposed. Based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D), Hybrid-MOEA/D-Ihybrids a genetic algorithm and a differential evolutionary algorithm to effectively optimize sub-problems of the multi-objective optimization problem in WSN. By integrating a discrete particle swarm algorithm, we further enhance solutions generated by Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm. Simulation results show that the proposed Hybrid-MOEA/D-I and Hybrid-MOEA/D-II algorithms have a significant better performance compared with existing algorithms in the literature in terms of all the objectives concerned.

Journal Article Type Article
Acceptance Date Mar 30, 2018
Online Publication Date Apr 5, 2018
Publication Date 2018-07
Deposit Date May 14, 2018
Publicly Available Date Apr 6, 2019
Journal Applied Soft Computing
Print ISSN 1568-4946
Electronic ISSN 1872-9681
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 68
Pages 268-282
DOI https://doi.org/10.1016/j.asoc.2018.03.053
Public URL https://nottingham-repository.worktribe.com/output/948602
Publisher URL https://www.sciencedirect.com/science/article/pii/S1568494618301868

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