Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization
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 netw...
| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/51582/ |
| _version_ | 1848798527742803968 |
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| author | Xu, Ying Ding, Ou Qu, Rong Li, Keqin |
| author_facet | Xu, Ying Ding, Ou Qu, Rong Li, Keqin |
| author_sort | Xu, Ying |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:21:12Z |
| format | Article |
| id | nottingham-51582 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:21:12Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-515822020-05-04T19:48:01Z https://eprints.nottingham.ac.uk/51582/ Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization Xu, Ying Ding, Ou Qu, Rong Li, Keqin 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. Elsevier 2018-07-30 Article PeerReviewed Xu, Ying, Ding, Ou, 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 https://www.sciencedirect.com/science/article/pii/S1568494618301868 |
| spellingShingle | Xu, Ying Ding, Ou Qu, Rong Li, Keqin Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| title | Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| title_full | Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| title_fullStr | Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| title_full_unstemmed | Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| title_short | Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| title_sort | hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization |
| url | https://eprints.nottingham.ac.uk/51582/ https://eprints.nottingham.ac.uk/51582/ |