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

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

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.

Citation

Xu, Y., Ding, O., Qu, R., & Li, K. (2018). Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Applied Soft Computing, 68, 268-282. https://doi.org/10.1016/j.asoc.2018.03.053

• 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