Thematische Bibliographien / Dragon fly algorithm

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Auswahl der wissenschaftlichen Literatur zum Thema „Dragon fly algorithm“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 4. Februar 2022

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Zeitschriftenartikel zum Thema "Dragon fly algorithm"

Sunny Kumar, A., T. V. Hanumantha Rao, V. V. S. Kesava Rao und R. T. RamaKanth. „Optimizing pulsed current micro plasma arc welding parameters to maximize ultimate tensile strength of titanium (Ti-6Al-4V) alloy using Dragon fly algorithm“. Materials Today: Proceedings 27 (2020): 2086–90. http://dx.doi.org/10.1016/j.matpr.2019.09.073.

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Zafar, Muhammad Hamza, Thamraa Al-shahrani, Noman Mujeeb Khan, Adeel Feroz Mirza, Majad Mansoor, Muhammad Usman Qadir, Muhammad Imran Khan und Rizwan Ali Naqvi. „Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading“. Electronics 9, Nr. 11 (20.11.2020): 1962. http://dx.doi.org/10.3390/electronics9111962.

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The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

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„Diabetic Foot Risk Classification using Decision Tree and Bio-Inspired Evolutionary Algorithms“. International Journal of Innovative Technology and Exploring Engineering 9, Nr. 2S (31.12.2019): 232–39. http://dx.doi.org/10.35940/ijitee.b1081.1292s19.

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Diabetic foot complications are a burden to the Indian population which affects both financially and physically. The complications could be prevented if the risk of diabetic foot are detected well in advance before the peripheral nerves are damaged leading to amputation and limb loss. The quantification of severity plays an important role in timely intervention, delivery of appropriate treatment and prevention of amputation. This can be modeled as a classification problem where the risk category is stratified into different levels of severity. This paper is an approach to build such a system, capable of classifying the risk category of diabetic patients for suitable follow-up and care. Decision trees are used for the same with features selected using bio-inspired evolutionary algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), FireFly (FF), Dragon Fly (DF) and Gravitational Search Algorithm (GSA). The overall accuracy is 77% but it identifies the low risk and high risk cases effectively with 97% and 89% respectively.

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Kuzhali, S. Elavaar, und D. S. Suresh. „Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis“. International Journal of Image and Graphics, 05.04.2021, 2150051. http://dx.doi.org/10.1142/s0219467821500510.

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For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

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Manju, S., und Helenprabha K. „Analysis Of Vegetation Classification Algorithms On Satellite Images And Medical Images“. Current Signal Transduction Therapy 15 (27.12.2019). http://dx.doi.org/10.2174/1574362415666191227154656.

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: In recent days, the remote sensing algorithms are used in the medical field for improving the visualization of the medical images. Because, the medical images are generally in the gray scale image format for better visualization the colour Doppler or spectrograms are used but they are expensive. To overcome this drawback the remote sensing algorithm is applied to the medical images to group the pixels and visualize in different colours. The image processing techniques is used to classify the vegetation region into 16 samples. The image pre-processing is done by Wiener filter to remove the noise. Feature extraction is carried out by Grey Level Co-occurrence Matrix (GLCM) and the spectral bands are optimized by Particle Swarm Optimization (PSO) .The classification of vegetation region is classified by Extreme Learning Machine. In this, the comparisons of the remote sensing algorithms like IRVM-MFO, ELM-DF and ELM-PSO for the Indian pines and Salinas Dataset. Among these the ELM- Dragon Fly algorithm produced the best results for both the sets. Hence, this ELM-DF is applied to the Brain tissue region segmentation. In this paper the analysis is performed to find the efficient method for vegetation classification by comparing with other methods. Simulations are carried out on two datasets such as Indian Pine and Salinas scene. Performance metrics such as accuracy, specificity, and sensitivity have been evaluated that show the efficiency of the proposed classifier.

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„Routing by HDF based Optimal Path Selection in Multipath WSNs“. International Journal of Recent Technology and Engineering 8, Nr. 3 (30.09.2019): 1435–39. http://dx.doi.org/10.35940/ijrte.b3713.098319.

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Multipath routing (MPR) is an effectual method for routing data on Wireless Sensor Networks (WSNs) since it offers security, reliability as well as load balancing (LB) that are particularly serious in the resource-constrained scheme like WSNs. This paper proposed a selecting optimal routing path in MPR using QoS for WSN. In the First phase, the network nodes are initialized. Next, the nodes are formed as a cluster which is known as cluster formation utilizing K-Medoid clustering algorithm. In the cluster formation, the cluster heads (CH) are chosen from each cluster using Grey wolf Optimization (GWO) algorithm. In the next stage, routing operation is performed, which is bifurcated into 2 sections as, multipath route selection, and optimal path selection (OPS). For multipath route selection, AOMDV protocol is used. Using this protocol, efficient multipath routes are chosen in the network. After several transmissions, a route might lose the quality of the link. So an optimal path is chosen from the existing routes in the network using Hybrid Dragon Fly (HDF) optimization. Performance metrics of the proposed work is compared with that of existing optimal path routings techniques. Results illustrate that our model exhibited better energy efficiency along with Network Lifetime when compared to the existing routing models

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Afzal, Asif, Ümit Ağbulut, Manzoore Elahi M. Soudagar, R. K. Abdul Razak, Abdulrajak Buradi und C. Ahamed Saleel. „Blends of scum oil methyl ester, alcohols, silver nanoparticles and the operating conditions affecting the diesel engine performance and emission: an optimization study using Dragon fly algorithm“. Applied Nanoscience, 12.09.2021. http://dx.doi.org/10.1007/s13204-021-02046-5.

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Dissertationen zum Thema "Dragon fly algorithm"

Hnízdilová, Bohdana. „Registrace ultrazvukových sekvencí s využitím evolučních algoritmů“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442502.

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This master´s thesis deals with the registration of ultrasound sequences using evolutionary algorithms. The theoretical part of the thesis describes the process of image registration and its optimalization using genetic and metaheuristic algorithms. The thesis also presents problems that may occur during the registration of ultrasonographic images and various approaches to their registration. In the practical part of the work, several optimization methods for the registration of a number of sequences were implemented and compared.

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Buchteile zum Thema "Dragon fly algorithm"

Goyal, Monika, Deepak Goyal und Sandeep Kumar. „Dragon-AODV: Efficient Ad Hoc On-Demand Distance Vector Routing Protocol Using Dragon Fly Algorithm“. In Advances in Intelligent Systems and Computing, 181–91. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0751-9_17.

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Konferenzberichte zum Thema "Dragon fly algorithm"

Kavitha Kumari, K. S., und R. Samuel Rajesh Babu. „Effective Microgrid Cost Reduction using Dragon Fly Optimization Algorithm and Firefly Algorithm“. In 2020 5th International Conference on Computing, Communication and Security (ICCCS). IEEE, 2020. http://dx.doi.org/10.1109/icccs49678.2020.9276979.

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Reddy, M. Satish Kumar, K. Narayanan und S. Jayalalitha. „Sequential and simultaneous reconfiguration and distributed generation installation using dragon fly algorithm“. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389567.

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Dotson, Corey, Geronimo Macias und Kooktae Lee. „Energy-Balanced Leader-Switching Policy for Formation Rotation Control of Multi-Agent Systems Inspired by Bird Flocks“. In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9044.

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Abstract This paper addresses an energy-balanced leader-switching policy for formation rotation control of multi-agent systems inspired by bird flocks. Birds that flock in V-formation with a leader rotation strategy are able to travel longer distances due to reduced drag and therefore less energy expenditure. This flocking behavior with a leader rotation will result in more conservation of overall energy and will be particularly beneficial to migrating birds that should fly long distances without landing. In this paper, we propose an energy-balanced leader-switching policy inspired by this bird flocking behavior in order to increase the flight range for multi-agent systems. The formation control of multi-agent systems is achieved by the consensus algorithm, which is fully decentralized through the use of information exchanges between agents. The proposed leader-switching method is not necessarily incorporated with the consensus dynamics and thus, the leader-switching algorithm can be decoupled from formation control dynamics. Therefore, the proposed method can simplify the leader-switching algorithm, making it easy to implement. Moreover, we propose the analytic flight distance based on the energy consumption model for each agent. To test the validity of the developed method, several simulation results are presented.

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