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Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

ENGINEERING WITH COMPUTERS 2021年第3期37卷 1943-1959页

作者： Chen, Siyu Gu, Chongshi Lin, Chaoning Zhang, Kang Zhu, Yantao Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili Nanjing 210098 Peoples R China

The observation data of dam displacement can reflect the dam's actual service behavior intuitively. Therefore, the establishment of a precise data-driven model to realize accurate and reliable safety monitoring of dam deformation is necessary. This study proposes a novel probabilistic prediction approach for concrete dam displacement based on optimized relevance vector machine (ORVM). A practical optimization framework for parameters estimation using the parallel jaya algorithm (PJA) is developed, and various simple kernel/multi-kernel functions of relevance vector machine (RVM) are tested to obtain the optimal selection. The proposed model is tested on radial displacement measurements of a concrete arch dam to mine the effect of hydrostatic, seasonal and irreversible time components on dam deformation. Four algorithms, including support vector regression (SVR), radial basis function neural network (RBF-NN), extreme learning machine (ELM) and the HST-based multiple linear regression (HST-MLR), are used for comparison with the ORVM model. The simulation results demonstrate that the proposed multi-kernel ORVM model has the best performance for predicting the displacement out of range of the used measurements dataset. Meanwhile, the ORVM model has the advantages of probabilistic output and can provide reasonable confidence interval (CI) for dam safety monitoring. This study lays the foundation for the application of RVM in the field of dam health monitoring.

关键词： Optimized relevance vector machine Multi-kernel jaya optimization algorithm Dam health monitoring Prediction model

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A novel algorithm for computing interior eigenpairs of large non-symmetric matrices

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SOFT COMPUTING 2021年第17期25卷 11865-11876页

作者： Jamalian, Ali Aminikhah, Hossein Univ Guilan Dept Comp Sci Rasht Iran Univ Guilan Dept Appl Math Rasht Iran Univ Guilan Ctr Excellence Math Modelling Optimizat & Combina POB 41938-19141 Rasht Iran

One challenging problem in numerical linear algebra is computing eigenvalues from the interior of the spectrum of large matrices. The harmonic Arnoldi method is one of the methods that can be used to find interior eigenpairs of a large non-symmetric matrix. However, it has been shown that this method may converge erratically and even may fail. In this paper, we present a novel and efficient method for computing interior eigenpairs of large non-symmetric matrices. The proposed eigensolver is a hybrid of the harmonic Arnoldi algorithm and the jaya optimization algorithm, which is called jaya-HA. Numerical experiments have tested the accuracy and performance of the jaya-HA, and computational results show that this algorithm converges faster and provides superior performance.

关键词： Harmonic Arnoldi method jaya optimization algorithm Interior eigenproblem Eigenpair Non-symmetric matrix

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Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2020年第1期30卷 234-251页

作者： Mahesh, Michael K. Renjit, Arokia J. St Joseph Coll Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Jeppiaar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional jaya optimizer-deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.

关键词： deep convolutional neural networks jaya optimization algorithm multimodal MRI brain images particle swarm optimization severity-level classification

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Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with jaya algorithm

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Information Processing in Agriculture 2020年第2期7卷 249-260页

作者： S.Ramesh D.Vydeki Electronics and Communication Engineering Department VIT UniversityChennai 600127India

In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a *** recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural *** this paper,Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with jaya algorithm is *** the image acquisition the images of rice plant leaves are directly captured from the farm field for normal,bacterial blight,brown spot,sheath rot and blast *** pre-processing,for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased *** the segmentation of diseased portion,normal portion and background a clustering method is *** of diseases is carried out by using Optimized Deep Neural Network with jaya optimization algorithm(DNN_JOA).In order to precise the stability of this approach a feedback loop is generated in the post processing *** experimental results are evaluated and compared with ANN,DAE and *** proposed method achieved high accuracy of 98.9%for the blast affected,95.78%for the bacterial blight,92%for the sheath rot,94%for the brown spot and 90.57%for the normal leaf image.

关键词： Paddy leaf diseases Optimized Deep Neural Network jaya optimization algorithm K-means clustering Color features Texture features

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Evolutionary computing for clinical dataset classification using a novel feature selection algorithm

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 5075-5082页

作者： Sheth, Pranali D. Patil, Shrishailappa T. Dhore, Manikrao L. Savitribai Phule Pune Univ Vishwakarma Inst Technol Dept Comp Engn Pune India

The medical diagnostic decision support system uses machine learning and data mining algorithms to detect and diagnose diseases. Several deaths can be avoided if the diseases are detected and cured in the early stages of infection. Feature selection is a major pre-processing method used to obtain the most significant features, thereby enhancing the data mining model's classification accuracy. This work proposes a new feature selection algorithm to perform feature selection as a multi-objective optimization problem. The minimization of classification error rate and minimization of the feature subset's cardinality are the two contradictory objectives that need to be optimized simultaneously. The proposed work is applied for five clinical datasets such as lung cancer, breast cancer, diabetes, fertility, and immunotherapy and the results are compared with existing techniques based on 6 other datasets. This work converts the real-valued jaya optimization algorithm into binary space. It also handles premature convergence and sensitivity-specificity trade-off. The proposed algorithm's efficiency is assessed and analyzed based on average classification accuracy, sensitivity, specificity, number of features selected, percentage feature selection, and CPU computation time. The proposed algorithm improves the effectiveness of data mining based medical diagnostic decision support system.(c) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Classification Evolutionary algorithms Feature selection jaya optimization algorithm Multi -objective optimization

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Optimal Design of PID Controller Based Sampe-jaya algorithm for Load Frequency Control of Linear and Nonlinear Multi-Area Thermal Power Systems

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2020年第1期50卷 79-93页

作者： El Ela, Adel A. Abou El-Sehiemy, Ragab A. Shaheen, Abdullah M. Diab, Abd El Galil Menoufiya Univ Fac Engn Elect Engn Dept Menoufia Egypt Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafrelsheikh Egypt Suez Univ Fac Engn Elect Engn Dept El Suweis Egypt MDEPC El Buhayira Egypt

Modern multi-area power systems are in persistent facing to imbalances in power generation and consumption which directly causes frequency and tie-line power fluctuations in each area. This paper deals with the load frequency control (LFC) problem where the control objective of regulating their error signals despite the presences of several external load disturbances. It proposes an optimal design of proportional integral derivative controller (PID) based on a novel version of jaya algorithm called self-adaptive multi-population elitist (SAMPE) jaya optimizer. A filter with derivative term is integrated with PID controller to alleviate the impact of noise in the input signal. A time domain based-objective functions are investigated such as integral time-multiplied absolute value of the error (ITAE) and integral of absolute error (IAE). Both SAMPE-jaya and jaya optimizers are employed to optimally tune the PID parameters for interconnected power systems comprising two non-reheat thermal areas. Three test cases are performed with various load disturbances in both areas individually and simultaneaously. Also, the practical physical constraints related to generation rate constraint (GRC) with its nonlinearity characteristics are taken into account. In addition, the obtained results using the designed PID controller based on SAMPE-jaya are compared with various reported techniques. These simulated comparisons declare the great efficiency and the high superiority of the designed PID controller based on SAMPE-jaya.

关键词： Load Frequency Control (LFC) Multi-area power system jaya optimization algorithm Proportional integral derivative controller (PID) Self-adaptive multi-population elitist jaya (SAMPE-jaya) Nonlinearities

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Fuzzy analytic hierarchy process based generation management for interconnected power system

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SCIENTIFIC REPORTS 2024年第1期14卷 1-24页

作者： Varshney, T. Waghmare, A. V. Singh, V. P. Meena, V. P. Anand, R. Khan, Baseem Sharda Univ Dept EECE SSET Greater Noida 201310 Uttar Pradesh India Malaviya Natl Inst Technol Dept Elect Engn Jaipur 302017 Rajasthan India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Elect Engn Bengaluru India Hawassa Univ Dept Elect & Comp Engn Hawassa Ethiopia

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller's performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

关键词： Fuzzy AHP AHP jaya optimization algorithm PID controller AGC Power system

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A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions

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CHEMOSPHERE 2022年第Part3期307卷 135798页

作者： Yang, Hong Liu, Zehang Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

With the development of economy, the problem of air pollution has become increasingly serious. As an important detection index of air pollutants, how to accurately and effectively predict PM2.5 concentration is a significant issue related to human health and development. In this paper, a new hybrid optimization prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEM-DAN), variational mode decomposition optimized by COOT optimization algorithm (COOT-VMD), and least square support vector machine (LSSVM) optimized by the jaya optimization algorithm (jaya-LSSVM), named CEEMDAN-COOT-VMD-jaya-LSSVM, is proposed. To avoid artificially setting the limits of the decomposition layer and penalty factor of VMD parameters, an improved VMD by COOT optimization algorithm, named COOT-VMD, is proposed. First, the original sequence of PM2.5 concentration is decomposed by CEEMDAN. Second, the high complexity component with low prediction accuracy after once decomposition is decomposed by COOT-VMD. Third, a prediction model of optimized LSSVM by jaya optimization algorithm, named jaya-LSSVM, is proposed. jaya-LSSVM is used to predict all components considering other air pollutants such as PM10, SO2, NO2, CO, and O3 and meteorological conditions such as wind speed, temperature, sunlight, relative humidity and average air pressure. Finally, all predicted values are reconstructed to obtain the final prediction results. PM2.5 concentration from April 1, 2016 to March 29, 2021 in Xi'an and Shenyang is used as the experimental data to verify the proposed model. The results of experiment in Xi'an show that the RMSE, MAE, MAPE and R(2 )are 2.843, 1.8344, 2.94%, and 0.99525 respectively. The results of experiment in Shenyang show that the RMSE, MAE, MAPE and R2 are 2.2714, 1.673, 3.13%, and 0.99573 respectively. Compared to other single and hybrid models, the proposed model can accurately predict PM2.5 concentration. Diebold Mar

关键词： PM2.5 concentration prediction Secondary decomposition COOT optimization algorithm Least square support vector machine jaya optimization algorithm

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An energy-efficient topology-aware virtual machine placement in Cloud Datacenters: A multi-objective discrete jaya optimization

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2023年 38卷

作者： Shirvani, Mirsaeid Hosseini Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices;it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objective optimization problem with power consumption and resource wastage minimi-zation perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of jaya (MOD-jaya) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-jaya algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics.

关键词： Cloud computing Data center networks Virtual machine placement jaya optimization algorithm Multi-objective optimization algorithm Topology-aware Scheme

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Hysteresis Identification of Piezoelectric Actuator Using Neural Network Trained By jaya algorithm

Hysteresis Identification of Piezoelectric Actuator Using Ne...

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International Symposium on Electrical and Electronics Engineering (ISEE)

作者： Nguyen Ngoc Son Ho Pham Huy Anh Cao Van Kien Tran Minh Chinh Ind Univ HCM City Fac Elect Technol Ho Chi Minh City Vietnam HCM City Univ Technol VNU HCM FEEE Ho Chi Minh City Vietnam

ISBN: (纸本)9781728153537

The hysteresis nonlinearity widely existing in smart materials yields undesirable responses, which cause the hysteresis control problem even more challenging. Therefore, many studies based on the neural network have been introduced to cope with the hysteresis nonlinearity. However, the popular back-propagation algorithm used in training neural network model often performs local optima with stagnation and slow convergence speed. To overcome these drawbacks, this paper proposes a new training algorithm based on jaya technique to optimally identify the neural network weighting values. This approach is applied to estimate the nonlinear hysteresis loop of the piezo-based device. The identification results demonstrate that the proposed algorithm successfully identifies the highly nonlinear hysteresis with perfect precision.

关键词： Nonlinear Auto-Regressive eXogenous (NARX) Neural Network Hysteresis Nonlinearity Piezoelectric actuator jaya optimization algorithm

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