Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks

Lanza-Gutiérrez, José M.; Gómez–Pulido, Juan A.

doi:10.1016/j.asoc.2015.01.051

Cited by 58 publications

(29 citation statements)

References 52 publications

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“…The honey bee swarm consists of three groups of bees, namely the employed bees, onlookers and scouts. Correspondingly, the ABC algorithm has three phases [151], [210]. It is assumed that there is only a single artificial employed bee for each food source.…”

Section: B Moo Algorithmsmentioning

confidence: 99%

“…For the sake of clarity, some representative MOO algorithms are summarized in Table IV, including the MOGA [191], NPGA [194], NSGA [193], NSGA-II [21], SPEA [16], the strength Pareto evolutionary algorithm-2 (SPEA2) [220], the multi-objective messy genetic algorithm (MOMGA) [221], the multi-objective messy genetic algorithm-II (MOMGA-II) [222], the Bayesian optimization algorithm (BOA) [223], the hierarchical Bayesian optimization algorithm (HBOA) [224], the Pareto archive evolution strategy (PAES) [225], the Pareto envelope-based selection algorithm (PESA) [226], the Pareto envelope-based selection algorithm-II (PESA-II) [227], multi-objective differential evolution (MODE) [196], multi-objective evolutionary algorithm based on decomposition (MOEA/D) [228]. Additionally, there are some other methods, such as the multi-objective genetic local search (MOGLS) [229], the multi-objective Tabu search (MOTS) [230], the multi-objective scatter search (MOSS) [231], ACO [24], PSO [205], ABC [151], FL [37], ANN, AIS, game theory [219], MOICA [157], memetic algorithm (MA) [232], and centralized immune-Voronoi deployment algorithm (CIVA) [116], just to name a few.…”

Section: B Moo Algorithmsmentioning

confidence: 99%

“…In [264], Razzaque et al proposed a QoS-aware routing protocol for body sensor networks, in which a lexicographic optimization approach was used for trading off the QoS requirements and energy costs. In [151], Lanza-Gutierrez et al considered the deployment of energy harvesting relay nodes for resolving the conflict among average energy cost, average sensing area and network reliability. Two multi-objective metaheuristics, i.e., the ABC algorithm and the firefly algorithm (FA), were applied for solving the problem, respectively.…”

Section: E Reliability-related Trade-offsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

382

202

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Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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Section: B Moo Algorithmsmentioning

confidence: 99%

Section: B Moo Algorithmsmentioning

confidence: 99%

Section: E Reliability-related Trade-offsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

382

202

View full text Add to dashboard Cite

show abstract

“…Lanza and Gómez use in [26] a methodological approach to compare different algorithms implementation, considering the regularity and consistency of their results, as is shown in Fig. (1).…”

Section: Statistical Comparison Approachmentioning

confidence: 99%

Comparing the Black Hole and the Soccer League Competition Algorithms Solving the Set Covering Problem

Jaramillo¹,

Gómez²,

Crawford³

et al. 2018

Polibits

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The development of techniques to solve the Set Covering Problem (SCP) have given rise a wide range of metaheuristic alternatives, some of them designed from the beginning to operate in binary search spaces, and other considering continuos spaces that requires adaptation intended to work with binary spaces. Black Hole and Soccer League Competition they were designed to work with continuous spaces and they have been adapted to operate in binary spaces: Binary Black Hole and Binary Soccer League Competition, respectively, aimed to solve problems in a binary domain, particularly the SCP. The present paper compare both implementation in a statistical way, involving the use of non-parametric tests and supported by R statistical computing enviroment, considering regularity and consistency of their results when both algorithm implementations are tested on the same benchmark sets.

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“…In addition to all the aforementioned heuristic based applications, ant colony with greedy migration has also been utilized to deploy sensor nodes with a view to maximizing coverage and minimizing network deployment cost. A multi-objective approach has also been utilized to solve RN placement problem by optimizing three conflicting objectives -network reliability, cost, and average sensitivity area [34]. Unlike [35,33,32] that mainly focused on finding the optimal sensor node positions that enhance the network lifetime, heuristic-based RN deployment here proposed focuses on improving the network lifetime by deploying fewest number of RNs while satisfying connectivity constraint .…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

Ayinde

Hashim

2018

Int J Wireless Inf Networks

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Random deployment of sensor nodes is susceptible to initial communication hole, even when the network is densely populated. However, eliminating holes using structural deployment poses its difficulties. In either case, the resulting coverage holes can degrade overall network performance and lifetime. Many solutions utilizing Relay Nodes (RNs) have been proposed to alleviate this problem. In this regard, one of the recent solutions proposed using Artificial Bee Colony (ABC) to deploy RNs. This paper proposes RN deployment using two other evolutionary techniques -Gravitational Search Algorithm (GSA) and Differential Evolution (DE) and compares them with existing solution that uses ABC. These popular optimization tools are deployed to optimize the positions of relay nodes for lifetime maximization. Proposed algorithms guarantee satisfactory RNs utilization while maintaining desired connectivity level. It is shown that DE-based deployment improves the network lifetime better than other optimization heuristics considered.

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Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks

Cited by 58 publications

References 52 publications

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Comparing the Black Hole and the Soccer League Competition Algorithms Solving the Set Covering Problem

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

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