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

•This paper models the coverage control optimization problem in WSN as a multi-objective optimization problem considering multiple objectives.•A hybrid reproduction operator based on Genetic Algorithm (GA) and Differential Evolution (DE) has been proposed to diversify the search.•An discrete binary...

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Veröffentlicht in:Applied soft computing Jg. 68; S. 268 - 282
Hauptverfasser: Xu, Ying, Ding, Ou, Qu, Rong, Li, Keqin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2018
Schlagworte:
Coverage optimization
MOEA/D
Multi-objective optimization
Wireless sensor networks
Wireless sensor networks
Coverage optimization
Multi-objective optimization
MOEA/D
ISSN:1568-4946, 1872-9681
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Zusammenfassung:•This paper models the coverage control optimization problem in WSN as a multi-objective optimization problem considering multiple objectives.•A hybrid reproduction operator based on Genetic Algorithm (GA) and Differential Evolution (DE) has been proposed to diversify the search.•An discrete binary particle swarm optimization method has been designed as the enhancement strategy to obtain a better Pareto solution set.•Large amount of experiments have been carried out to demonstrate the effectiveness of our proposed algorithms. 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 lifetime 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-I hybrids 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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.03.053