Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm

Shilaja, C.; Thiyagarajan, Arunprasath

doi:10.1016/j.future.2018.12.070

Cited by 31 publications

(9 citation statements)

References 12 publications

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“…Determine initial segmentation points using dragonfly algorithm The behavior of swarms follows three primitive principles [14,15,43,44]: (1) Separation principle, this principle relies on the fact that the swarm individuals tries to avoid static collision with other individuals in the neighborhood. (2) Alignment principle, this principle reflects the attitude of the each individual in the swarm to match its velocity with other individuals in the neighborhood; and (3) Cohesion principle, this principle refers to the tendency of swarm individuals to centralized around the neighborhood mass center.…”

Section: Beginmentioning

confidence: 99%

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

et al. 2020

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Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

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Section: Beginmentioning

confidence: 99%

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

et al. 2020

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“…Intelligence algorithms perform intelligent behavior by collecting conditioning factors to solve problems. The Dragonfly Algorithm (DA), one of the pioneer intelligence algorithms, has been extensively studied in the recent years (Mirjalili 2016; KS and Murugan 2017; Jafari and Chaleshtari 2017;Díaz-Cortés et al 2018;Shilaja and Arunprasath 2019;Li et al 2020). It is a meta-heuristic optimization algorithm that was developed using the particle swarm optimization technique with distinctive and extraordinary swarming behavior, which is intended to represent a tiny predator in nature, because of its simple and easy implementation.…”

Section: Dragonfly Algorithm (Da)mentioning

confidence: 99%

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

et al. 2020

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Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

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“…The authors would apply the Dragonfly Algorithm in this analysis, based on the Dragonfly algorithm principle that begins statically as a starting point and energetic behavior of the dragonfly herd. At the two main stages of optimization via the heuristic meta-algorithm, this clustered action is used as a parable, namely inquiry and use [9]. The optimization strategy in meta-heuristic hybrids, DA (Dragonfly Algorithm), deals with optimization power flow problems in the study guide.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm stated is applied to obtain the optimum value variable power system control and address OPF issues. It will minimize power loss, voltage profile variations and monitor fuel costs, which are the critical goals of the OPF problem [9]. There is, however, no study using the Dragonfly algorithm on economic optimization and pollution.…”

Section: Introductionmentioning

confidence: 99%

“…There have been some previous reports to deal with the OPF (optimal power flow) problem. Shilaja C et al used the Dragonfly algorithm and analyzed the IEEE 30 bus system [9]. The Dragonfly algorithm was used by Sureshkumar et al to mitigate device errors based on objective power flow control functions such as actual power and reactive power [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm

Wibowo

Handoko

Hermawan

2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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Electricity is one of the energies required by daily living since the greater demand for electricity increases greenhouse emissions that create emission gases resulting in global climate change. The main portion of the output cost is fuel's cost to manufacture electrical energy in thermal turbines. The use of electrical energy is currently rising increasingly following the increasing population. The research aims to optimize hydro generation to minimize thermal generation expense and address economic problems and pollution from shipping. With 2016b using Matlab applications and the lambda iteration process, the analysis method uses the Dragonfly Algorithm method. The analysis found that the average cost of fuel consumption provided by the Dragonfly Algorithm method was IDR 151,164,418 per day with an emission of 917.40 tons per day, based on the simulation results the Dragonfly Algorithm in testing by considering the emission of 5 practical steps. Meanwhile, with the emission of 918,044 tonnes per day, the average cost of fuel consumption produced by the Lambda Iteration method is IDR 151,202,209 per day. Test results can enhance the fuel consumption cost of IDR 37,791 and emissions of 0.641 tons with the Dragonfly Algorithm process.

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Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm

Cited by 31 publications

References 12 publications

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm

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