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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： P. D. Dewangan Praveen Mande Ankur Gupta P. J. Krishna V. P. Meena V. P. Singh Department of Electrical Engineering NIT Raipur India Department of EECl Engineering Gandhi Institute of Tech. and Manag India School of CSE and Tech. Bennett University India Department of Electrical Engineering Malaviya National Inst. of Technology Jaipur India Department of Electrical Engineering Malaviya National Institute of Technology India Department of Electrical Engineering MNIT Jaipur India

ISBN: (纸本)9781450399937

In this work, the efficiency and performance of the jaya optimizer are evaluated for two different initialization methods. In the course of the entire iterative process, these starting approaches are used whenever an aspect of any explanation violates the exploration area. The violating aspect is initially set up in the first technique close to the edge of the exploration area. In the second, the violating aspect of the exploration area is initialized at irregularly. For performance comparison, eight different unimodal measure exercises are reduced using the modified jaya approach. The appraisal of the evenhanded exercise, or figure of merit, along with its mean, minimum, standard deviation, and maximum appraisals are used to compare efficacy and performance.

关键词： Swarm Intelligence Initialization Meta-heuristics Unimodal measure exercises jaya optimization algorithm

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optimization free of algorithm-specific control parameters for Power System Stabilizer tuning 61

Optimization free of algorithm-specific control parameters f...

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61st IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga-Technical-University (RTUCON)

作者： Gonzalez, M. Ramirez Sevilla, F. R. Segundo Korba, P. ZHAW Zurich Univ Appl Sci Inst Energy Syst & Fluid Engn Winterthur Switzerland

ISBN: (纸本)9781728195100

A settings-free and derivative-free optimization technique, called jaya optimization algorithm, is investigated in this paper for the design of simple fuzzy PSSs (FPSSs) in a multimachine power system. Performance of this technique during the optimization process is compared to the one exhibited by a Bat optimization algorithm based approach. Considering a set of test cases for illustrative purposes, FPSSs tuned with both of these algorithms are applied to the study system and their performance in damping inter-area and local oscillations is analyzed against some commonly used lead-lag structures, including multi-band PSSs.

关键词： Power system stabilizer fuzzy logic parameter tuning settings-free optimization jaya optimization algorithm

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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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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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Online load frequency control in wind integrated power systems using modified jaya optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 77卷 212-228页

作者： Pradhan, Chittaranjan Bhende, Chandrashekhar N. Indian Inst Technol Sch Elect Sci Bhubaneswar 751013 India

The jaya optimization algorithm is recognized as a simple and faster population-based heuristic search algorithm. However, due to the absence of algorithm-specified parameter, its performance may degrade in terms of convergence speed and obtain the optimal value for the real-world complex optimization problems which are usually nonlinear and non-differentiable in nature. To deal with the aforesaid scenarios, a modified jaya optimization algorithm (MJOA) is proposed by considering a weight parameter in the search process. Two methods are proposed on the basis of the selection of weight parameter, one is a linear weight (LW) and the other is a fuzzy logic based mechanism. It is found that MJOA is quite accurate and faster in convergence for complex problems. The proposed MJOA has used for online tuning the controller parameters of automatic generation control (AGC) of wind integrated power system. Further, a frequency deviation tolerance concept is proposed to reduce the number of executions of MJOA. The proposed methodology helps in recovering the frequency excursions of the power system with a faster convergence performance and requirements of less computational burden. Such features are very important in smart-grid operational necessities for improving its stability performance. The real-time simulation studies are carried out on an embedded platform using xPC target board of the wind farm integrated IEEE-39 bus test system.

关键词： Fuzzy logic jaya optimization algorithm Load frequency control Real-time optimization Wind integrated power system

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Optimal Operation of Synchronous Motor Using Particle Swarm optimization and jaya Techniques 21

Optimal Operation of Synchronous Motor Using Particle Swarm ...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdelwanis, Mohamed, I Selim, F. Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafrelsheikh Egypt

ISBN: (纸本)9781728152899

This paper concerns with the application of optimized operation of synchronous motor problem (SM) in industrial power system applications. This represents an active area of research, and many modeling and computational techniques are periodically being proposed for solving these problems. Therefore, this paper proposes optimization procedure for the operation of (SM) using Particle Swarm optimization (PSO) and jaya optimization techniques. The optimal operation of synchronous motor by using PSO and jaya is suggested with constant outputs to minimize the motor armature current, maximize the motor efficiency, minimize the synchronous motor input apparent power and reactive power with reducing of the copper losses. The analysis of results explains the benefits of using the optimization methods (PSO, jaya) with respect to the traditional method especially jaya optimization results. The implementation work (practical) is carried out in laboratory of faculty of engineering in Kafrelsheikh University.

关键词： jaya optimization algorithm Optimal operation Particle swarm optimization technique (PSO) Synchronous motor (SM)

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Application of jaya algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA

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ENGINEERING WITH COMPUTERS 2017年第3期33卷 457-475页

作者： Abhishek, Kumar Kumar, V. Rakesh Datta, Saurav Mahapatra, Siba Sankar ICFAI Fdn Higher Educ Fac Sci & Technol Dept Mech Engn Hyderabad 500082 Telangana India Natl Inst Technol Dept Mech Engn Rourkela 769008 India

With the widespread application of carbon fibre-reinforced polymer (CFRP) composites, mostly in defence, automotive, and aerospace industries, the machining of those materials has become a major concern today. As the machinability of those composites differs from the conventional metals, a proper understanding of process behaviour and identification of the favourable machining environment (optimal setting of process parameters) are indeed necessary to improve product quality. The present work highlights the application potential of a multi-response optimization route by integrating nonlinear regression modelling, fuzzy inference system (FIS) in combination with the jaya optimization algorithm, for the selection of optimal process parameter setting during the machining (turning) of carbon fibre-reinforced (epoxy) composites. Experiments have been carried out in consideration with spindle speed, feed rate, and depth of cut as process control parameters, whereas material removal rate (MRR), roughness average (R (a)), and net cutting force have been treated as machining performance characteristics. Attempt has been made to identify the best setting of process parameters for optimizing aforesaid output responses, simultaneously. The result of the jaya algorithm has also been compared to that of TLBO (teaching-learning-based optimization) algorithm. In addition to this, the result obtained thereof has also been compared to that of two evolutionary optimization algorithms viz., GA (genetic algorithm) and ICA (imperialist competitive algorithm). Good agreement has been observed amongst the obtained results. The aforesaid case experimental study thus exhibits the application potential of a newly developed jaya algorithm in the context of machining performance optimization during the turning of CFRP composites. The jaya algorithm is basically a parameter-less optimization algorithm which does not require any algorithm-specific parameter and hence easy to implement.

关键词： Carbon fibre-reinforced polymer (CFRP) Nonlinear regression modelling Fuzzy inference system (FIS) jaya optimization algorithm TLBO (teaching-learning-based optimization) GA (genetic algorithm) ICA (imperialist competitive algorithm)

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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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Enhancement of Artificial Emotional Neural Network Using jaya algorithm and the Investigation of Expanded Feature Selected for Wind Power Forecasting

Enhancement of Artificial Emotional Neural Network Using JAY...

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2019第七届计算机与通信管理国际会议

作者： Suthasinee Iamsa-at Punyaphol Horata Khamron Sunat Department of Computer Science Faculty of Science Khon Kaen University

The Brain Emotional Learning(BEL) is a novel bio-inspired machine learning approach mentioned as a new class of artificial neural network(ANN).The artificial emotional neural network(AENN) is one of the BEL methods which used the genetic algorithm(GA) to compute proper weights,weights of the amygdala(AMYG),orbitofrontal cortex(OFC) weights and a bias *** trained by GA has been reported that it could produce low error ***,AENN still has more rooms to enhance its prediction of performance,especially generalization and the prediction of ***,this paper aims to propose a new training method for *** jaya optimization algorithm optimized the weights and the biases of *** new proposed models are named as AENN-Maxjaya and *** names are according to the way of selecting the additional expanded feature which obtained through either the max or the average of input patterns,*** the experimental results for wind power forecasting dataset,the proposed methods proved that the results are better in generalization performance and give lower error rates which compared to the comparative AENN models and traditional ANNs.

关键词： Brain Emotional Learning artificial emotional neural network jaya optimization algorithm forecasting wind power

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