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

Xu, Ying and Ding, Ou and Qu, Rong and Li, Keqin (2018) Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Applied Soft Computing, 68 . pp. 268-282. ISSN 1872-9681

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/948602
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Qu, Rong
Date Deposited: 14 May 2018 12:54
Last Modified: 04 May 2020 19:48
URI: http://eprints.nottingham.ac.uk/id/eprint/51582

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