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Franklin Open 2025年 11卷

作者： Sen, Dipanwita Mishra, Bhupati Bhusan Pattnaik, Prasant Kumar KIIT Deemed To Be University Odisha Bhubaneswar 751024 India

Healthcare datasets are very sensitive datasets. In case of unauthorized access, sensitive datasets could potentially cause damage, discrimination and unsolicited scrutiny. The patients’ health details constitute private personal information. They should not be disclosed. However, data might get stored in cloud without any protection. That is why, privacy preservation of data for healthcare dataset is a significant consideration. In this work, the Wisconsin Prognostic Breast Cancer (WBC) dataset has been used. At first, a privacy preserving schema making use of perturbation implementing jaya algorithm has been elucidated. Out of 30 numerical attributes in the dataset, 6 are chosen for perturbation based on their relatively high Pearson's Correlation coefficient values. They form the initial population of jaya algorithm. The objective function is defined and we opt for a minimization problem. After each iteration, the algorithm generates a new population from the previous population. Thereafter, the accuracies obtained by a few traditional classification algorithms as well as classifiers based on some meta-heuristic algorithms, are observed . The classical classifiers used are Decision Tree, Random Forest, AdaBoost, KNN and GNB. The standard evaluation metrics are recorded thereafter. For privacy, the evaluation metrics used are Secrecy, Value Difference(VD), RP, RK, CP and CK. For utility, the metrics are Accuracy, Precision, Recall, F1-Score and Area Under the Curve(AUC). jaya algorithm is then compared with traditional perturbation algorithms like 2DRT and 3DRT. It is seen that jaya preserves more privacy and retains more utility as suggested by mean Friedman Test Rankings. Among the metaheuristic optimization based classifiers, only the Dragonfly inspired Classifier(DIC) classifies over 90 % of the records correctly for the perturbed dataset. For classification, the perturbed dataset is fed into the DIC as the original population. The target is to minimize the di

关键词： 2DRT 3DRT CK CP Data mining (DM) Dragonfly Independent component analysis (ICA) jaya algorithm Meta-heuristic optimization Particle Swarm Optimization(PSO) Perturbation Privacy preserving data mining (PPDM) RK RP Secrecy VD

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jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2024年 33卷 5909-5921页

作者： Dele-Afolabi, T. T. Jung, D. W. Ahmadipour, Masoud Hanim, M. A. Azmah Adeleke, A. O. Kandasamy, M. Gunnasegaran, Prem Univ Tenaga Nas Inst Power Engn Kajang 43000 Selangor Malaysia Ajayi Crowther Univ Fac Engn Dept Mech Engn PMB 1066 Oyo Oyo State Nigeria Jeju Natl Univ Dept Mech Engn 1 Ara 1 Dong Jeju 690756 South Korea Univ Teknol MARA Coll Engn Sch Elect Engn Shah Alam 40450 Selangor Malaysia Univ Putra Malaysia Fac Engn Dept Mech & Mfg Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn Adv Engn Mat & Composites Res Ctr AEMC Serdang 43400 Selangor Malaysia Univ Tenaga Nas Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia SIMATS Saveetha Sch Engn Dept Electrochem Chennai 602105 Tamil Nadu India Sethu Inst Technol Dept Chem Kariapatti 626115 Tamil Nadu India

Chemical attack is one of the most significant issues affecting porous ceramic systems employed as membranes for separation technologies, which necessitate frequent system reliability testing. In this work, the non-linear predictive power of a hybridized machine learning prediction model, specifically jaya-XGBoost to predict the corrosion-induced mass loss of monolithic and nickel-reinforced porous alumina ceramics has been examined. This study demonstrates the mass loss of monolithic and Ni-reinforced porous alumina developed using rice husk and sugarcane bagasse in acidic and alkaline corrosive media. Based on empirical findings, the formation of a very stable Ni3Al2SiO8 spinelloid phase in the RH-graded composites increased their chemical stability in the corrosive environments compared to their monolithic and corresponding SCB-graded counterparts. Corrosion testing data of these specimens were collected and fitted into both XGBoost and jaya-XGBoost machine learning algorithms. The results showed that the jaya-XGBoost model performed better in predicting the corrosion- induced mass loss of both the monolithic and the nickel-reinforced porous alumina than the regular XGBoost model in terms of statistical accuracy measures. The jaya-XGBoost model developed in this study effectively predicted the mass loss in NaOH (R2 = 0.9984;MAE = 0.0168) and mass loss in H2SO4 (R2 = 0.9824;MAE = 0.0217) of the monolithic and nickel-reinforced porous alumina. The precision that can be obtained by modifying hyper-parameters with the jaya method, combined with the well-known accuracy of XGBoost, renders the proposed model novel.

关键词： Porous alumina Composites Corrosion resistance Agro-waste PFAs Extreme gradient boosting jaya algorithm

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jaya algorithm-based energy management system for battery- and ultracapacitor-powered ultralight electric vehicle

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第6期44卷 4977-4985页

作者： Demircali, Akif Koroglu, Selim Pamukkale Univ Dept Elect Elect Engn Denizli Turkey

To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real-time applications. The simulation results show that jaya-EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO-EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real-time applications.

关键词： battery electric vehicle energy management strategy jaya algorithm ultracapacitor

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jaya algorithm-Based Energy Management for a Grid-Connected Micro-Grid with PV-Wind-Microturbine-Storage Energy System

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2023年 63卷 159-184页

作者： Gbadega, Peter Anuluwapo Sun, Yanxia Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

In this study, the jaya optimization algorithm is used to address the micro-grid energy management optimization problem using a hybrid PV-wind-microturbine-storage energy system. The main goals of this study are to reduce environmental pollution, increase microturbine operating efficiency, and minimize the cost of power generated. The overall objective of the proposed optimization method employed in the PV-WECS system is to run the PV-WECS systems at full capacity while running the microturbine when the PV-WECS systems are unable to produce all of the required power. The amount of emissions and costs of generated energy are reduced when BESS is used in the microgrid system. Furthermore, it is observed from the results that there is about 61.39% cost saving in the micro-grid operational costs and 38% carbon emissions reductions using the proposed optimization algorithm compared to the other metaheuristic algorithms used in this study. To demonstrate the appropriateness and supremacy of the proposed algorithm over the various optimization techniques for energy management of the proposed micro-grid systems, simulation results from the proposed algorithm are compared with those from other population-based metaheuristic algorithms, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Genetic algorithms (GA). The proposed algorithm outperforms and produces better results than the existing metaheuristic optimization techniques. More importantly, it illustrates the viability and efficacy of the proposed jaya optimization approach in addressing the issue of energy management for large-scale power systems.

关键词： Energy management system jaya algorithm energy storage system multi-objective optimization metaheuristic algorithms MATLAB simulation

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jaya algorithm- a practical algorithm for solving economic load dispatch problems

Jaya Algorithm- a practical algorithm for solving economic l...

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International Conference on Energy and Power Engineering (ICEPE)

作者： Fahim, Khairul Eahsun Yassin, Hayati De Silva, Liyanage C. Roy, Tanmoy Rihan, Naahi Mumtaj Tanvir, Mohammad Anjum Univ Brunei Darussalam Bandar Seri Begawan Brunei ZNRF Univ Management Sci Dhaka Bangladesh Univ Brunei Daussalam Fac Integrated Technol Bandar Seri Begawan Brunei Anwer Khan Modern Univ EEE Dept Dhaka Bangladesh BRAC Univ EEE Dept Dhaka Bangladesh Nanchang Hangkong Univ Aeronaut Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665455770

A power system's generation, transmission, and distribution components all require some form of optimization to ensure efficiency, quality, safety, and stability. One of the critical issues in designing and managing an electric power generation system is the efficient scheduling of all power generation facilities to satisfy the increasing demand for electricity. Dynamic economic load dispatch includes a strategy for optimizing fuel usage. It can operate at its best by correctly scheduling the generator following the load requirement. The Lagrange multiplier method and Genetic algorithm, as well as assessments using the jaya algorithm, are compared in this research to find the lowest fuel cost while considering valve point effects. The suitability of the jaya algorithm for economic load dispatch has not been explored in much literature. So, one of this paper's prime objectives is to determine the suitability of the jaya algorithm for the real-world economic load dispatch problem.

关键词： Economic Load Dispatch (ELD) Genetic algorithm (GA) Lagrange Multiplier Method jaya algorithm

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jaya algorithm Based Optimal Allocation of Distributed Energy Resources 1st

Jaya Algorithm Based Optimal Allocation of Distributed Energ...

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1st International Conference on Intelligent Computing Techniques for Smart Energy Systems (ICTSES)

作者： Kumawat, Manoj Gupta, Nitin Jain, Naveen Bansal, R. C. MCE Dept Elect & Elect Engn Motihari 845401 India MNIT Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India MPUAT Dept Elect Engn CTAE Udaipur 313001 Rajasthan India Univ Sharjah Dept Elect & Comp Engn Sharjah U Arab Emirates

ISBN: (纸本)9789811502149;9789811502132

In this study, energy demand growth with environment issues is considered towards the environment-friendly planning of smart distribution network. Therefore, Distributed Energy Resource (DERs) can be located to supply the required system energy demand. This paper presents jaya algorithms that can deal with the placement of DERs with most effectively in radial distribution networks. This algorithm is a powerful optimization method which can be easily handled in the single-phase without any algorithms-specific control parameter. The strength of the proposed strategy is validated on two distribution networks as IEEE 33-bus and 69-bus test systems. The results are compared with already established methods as suggested in the literature. The result would be proved that the proposed approach is a robust approach that enhances the network performance by diminishing the demand for the distribution load within the constraints of the system.

关键词： Backward/forward power flow Distributed energy resource jaya algorithm Smart distribution system

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Multilayer Interval Type-2 Fuzzy Controller Design for Quadcopter Unmanned Aerial Vehicles Using jaya algorithm

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IEEE ACCESS 2020年 8卷 181246-181257页

作者： Le, Tien-Loc Quynh, Nguyen Vu Long, Ngo Kim Hong, Sung Kyung Sejong Univ Fac Mech & Aerosp Engn Seoul 143747 South Korea Lac Hong Univ Fac Mechatron & Elect Bien Hoa 810000 Vietnam

This study presents a multilayer interval type-2 fuzzy controller for controlling quadcopter unmanned aerial vehicles, and its main contribution is the design of a multilayer structure in the membership function, which can improve the learning ability and flexibility of the designed network. With the multilayer structure, the proposed network can reduce the number of rules while ensuring the control system performance. Also, the proposed network parameters are updated online using adaptive laws, which are obtained using the gradient descent method, and a Lyapunov stability function is used to guarantee system stability. Furthermore, the learning rates of the proposed network are optimized using the jaya algorithm. Numerical experimental results were conducted on the trajectory tracking control of a quadcopter unmanned aerial vehicle to illustrate the effectiveness of the proposed control method.

关键词： Interval type-2~fuzzy neural network quadcopter adaptive control unmanned aerial vehicle jaya algorithm

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Robust 2-DOF FOPID Controller Design for Maglev System Using jaya algorithm

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IETE JOURNAL OF RESEARCH 2020年第3期66卷 414-426页

作者： Sain, Debdoot Swain, Subrat Kumar Kumar, Tapas Mishra, Sudhansu Kumar Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India Birla Inst Technol Dept Elect & Elect Engn Mesra India Cambridge Inst Technol Dept Elect & Elect Engn Ranchi Bihar India

In this paper, 1-Degree of Freedom (1-DOF) and 2-Degree of Freedom (2-DOF) Integer Order (IO) and Fractional Order (FO) Proportional-Integral-Derivative (PID) Controller has been designed for the Magnetic Levitation (Maglev) system. Maglev is one of the most versatile research-oriented laboratory instruments in the field of control systems engineering. The controller for the Maglev system is designed in such a way that a ferromagnetic ball can precisely levitate in a controlled electromagnetic field with particular design specifications. The controller parameters can be appropriately identified by optimizing the objective function with various popular artificial intelligence based evolutionary optimization algorithms. The 2-DOF controller structure takes care of both reference signal tracking and disturbance rejection through the feedback path. The 1 and 2-DOF controller structures with both Integer as well as Fractional Order approaches for different nonlinear systems were already discussed in various papers. However, the application of the Fractional Order control technique on a highly nonlinear Maglev system using the Genetic algorithm (GA), Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (Hybrid PSO), and recently developed jaya algorithm is of our interest. Observation of the final analysis based on different simulation results states that as the controller structure moves from 1-DOF to 2-DOF, the performance of the system and the control signal gets improved. The robustness analysis has also been incorporated in this study which shows that the 1 and 2-DOF IOPID and FOPID controllers designed using different optimization algorithms for the Maglev plant are robust in nature.

关键词： Degree of freedom FOPID controller Genetic algorithm Hybrid particle swarm optimization IOPID controller jaya algorithm Magnetic levitation (Maglev) system Particle swarm optimization PID controller

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Sizing, layout and topology design optimization of truss structures using the jaya algorithm

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APPLIED SOFT COMPUTING 2018年 70卷 903-928页

作者： Degertekin, S. O. Lamberti, L. Ugur, I. B. Dicle Univ Civil Engn Dept TR-21280 Diyarbakir Turkey Politecn Bari Dipartimento Meccan Matemat & Management I-70126 Bari Italy Sirnak Univ Civil Engn Dept TR-73000 Sirnak Turkey

A very recently developed metaheuristic method called jaya algorithm (JA) is implemented in this study for sizing and layout optimization of truss structures. The main feature of JA is that it does not require setting algorithm-specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. The original JA formulation is modified in this research in order to improve convergence speed and reduce the number of structural analyses required in the optimization process. The suitability of JA for truss optimization is investigated by solving six classical weight minimization problems of truss structures including sizing, layout and large-scale optimization problems with up to 204 design variables. Discrete sizing/layout variables and simplified topology optimization also are considered. The test problems solved in this study are very common benchmarks in structural optimization and practically describe all scenarios that may be faced by designers. The results demonstrate that JA can obtain better designs than those of the other state-of-the-art metaheuristic and gradient-based optimization methods in terms of optimized weight, standard deviation and number of structural analyses. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Metaheuristic optimization methods jaya algorithm Sizing, layout and topology optimization Truss structures

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Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by jaya algorithm

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FUNDAMENTA INFORMATICAE 2017年第1-4期151卷 191-211页

作者： Wang, Shuihua Rao, Ravipudi Venkata Chen, Peng Zhang, Yudong Liu, Aijun Wei, Ling Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India Columbia Univ Dept Elect Engn New York NY 10027 USA Jiangsu Key Lab 3D Printing Equipment & Mfg Nanjing 210042 Jiangsu Peoples R China Arizona State Univ WP Carey Sch Business Tempe AZ 85287 USA Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200030 Peoples R China

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-of-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + jaya-FNN achieved sensitivity of 92.26% +/- 3.44%, specificity of 92.28% +/- 3.58%, and accuracy of 92.27% +/- 3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO.

关键词： fractional Fourier transform abnormal breast detection computer-aided diagnosis mammogram feedforward neural network jaya algorithm

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