Optimal power allocation in wireless sensor networks using emerging nature-inspired algorithms

Tsiflikiotis, Antonios; Goudos, Sotirios K.

doi:10.1109/mocast.2016.7495127

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…Finally, to compare TLBO‐Jaya with results from our earlier work, where the algorithms used were cat swarm optimization, cuckoo search, and PSO, we run TLBO‐Jaya with the same conditions as in the work of Tsiflikiotis and Goudos . TLBO‐Jaya emerges as the best algorithm in 7 out of the total 9 cases, where cat swarm optimization is best in 2 cases.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In this paper, we extend our earlier work and solve the above‐described problem by introducing a new hybrid optimization method named TLBO‐Jaya algorithm. We compare this algorithm with results from the literature, namely, the hybrid BBO‐DE algorithm proposed in the work of Boussaid et al Moreover, to further evaluate the algorithm's performance, we compare it with both the original TLBO and Jaya.…”

Section: Introductionmentioning

confidence: 91%

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Hybrid teaching‐learning optimization of wireless sensor networks

Tsiflikiotis

Goudos

Karagiannidis

2017

Trans Emerging Tel Tech

Self Cite

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This paper deals with the power allocation of decentralized detection in an optimal wireless sensor network (WSN). The main objective is to find a solution that minimizes the total power consumed by the WSN, so that the error probability at the fusion center would be below a certain threshold. More specifically, we propose a novel stochastic optimization algorithm, called the TLBO-Jaya algorithm, which is a hybrid form of two recently proposed algorithms, ie, the teaching-learning-based optimization (TLBO) and Jaya algorithms. The proposed optimization solution is evaluated for several WSN cases and compared with results from the literature. Additionally, it is compared with both the TLBO and Jaya algorithms, the heat transfer search algorithm, and the popular particle swarm optimization. Numerical results show that the proposed algorithm performs better than other well-known algorithms in almost all tested cases.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Hybrid teaching‐learning optimization of wireless sensor networks

Tsiflikiotis

Goudos

Karagiannidis

2017

Trans Emerging Tel Tech

Self Cite

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“…[33] Applied CSO on WSN in order to solve optimal power allocation problem PSO is marginally better for small networks. However, CSO outperformed PSO and cuckoo search algorithm [96] Applied CSO on WSN to optimize cluster head selection e proposed system outperformed the existing systems by 75%. [97] Applied CSO on CR based smart grid communication network to optimize channel allocation e proposed system obtains desirable results for both fairness-based and priority-based cases [98] Applied CSO in WSN to detect optimal location of sink nodes CSO outperformed PSO in reducing total power consumption.…”

Section: Computer Visionmentioning

confidence: 94%

“…Another concern in the context of WSN is minimizing the total power consumption while satisfying the performance criteria. So, Tsiflikiotis and Goudos addressed this problem which is known as optimal power allocation problem, and for that, three metaheuristic algorithms were presented and compared [96]. Moreover, Pushpalatha and Kousalya applied CSO in WSN for optimizing cluster head selection which helps in energy saving and available bandwidth [97].…”

Section: Wireless and Wsnmentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

100

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This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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“…Tsiflikiotis and Goudos [12] use the cat swarm optimization (CSO) to solve the optimal power allocation problem for decentralized detection in the wireless sensor networks. Seguel et al [13] solve the resource allocation optimization problem of indoor optical wireless communications by using genetic algorithm (GA) and cuckoo search (CS).…”

mentioning

confidence: 99%

Planning Optimization of the Distributed Antenna System in High-Speed Railway Communication Network Based on Improved Cuckoo Search

Chen¹,

Liu

Sun

et al. 2018

International Journal of Antennas and Propagation

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The network planning is a key factor that directly affects the performance of the wireless networks. Distributed antenna system (DAS) is an effective strategy for the network planning. This paper investigates the antenna deployment in a DAS for the highspeed railway communication networks and formulates an optimization problem which is NP-hard for achieving the optimal deployment of the antennas in the DAS. To solve this problem, a scheme based on an improved cuckoo search based on dimension cells (ICSDC) algorithm is proposed. ICSDC introduces the dimension cell mechanism to avoid the internal dimension interferences in order to improve the performance of the algorithm. Simulation results show that the proposed ICSDC-based scheme obtains a lower network cost compared with the uniform network planning method. Moreover, ICSDC algorithm has better performance in terms of the convergence rate and accuracy compared with the conventional cuckoo search algorithm, the particle swarm optimization, and the firefly algorithm.

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Optimal power allocation in wireless sensor networks using emerging nature-inspired algorithms

Cited by 7 publications

References 7 publications

Hybrid teaching‐learning optimization of wireless sensor networks

Hybrid teaching‐learning optimization of wireless sensor networks

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Planning Optimization of the Distributed Antenna System in High-Speed Railway Communication Network Based on Improved Cuckoo Search

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