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PI controller design using artificial bee colony algorithm for MPPT of photovoltaic system supplied DC motor-pump load

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COMPLEXITY 2016年第6期21卷 99-111页

作者： Oshaba, A. S. Ali, E. S. Abd Elazim, S. M. Power Elect & Energy Convers Elect Res Inst NRC Bldg Giza 12311 Egypt Jazan Univ Fac Engn Dept Elect Jazan Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt

Maximum Power Point Tracking (MPPT) is used in Photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV-DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by Artificial Bee Colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with Genetic algorithm for various disturbances to prove its robustness. (c) 2015 Wiley Periodicals, Inc. Complexity 21: 99-111, 2016

关键词： photovoltaic system optimization algorithm maximum power point tracking PI controller DC series motor pump system

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Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Wang, Haonan Wu, Danhong Luo, Jie Zhang, Junhui Southwest Med Univ Sch Publ Hlth 1Sect 1Xianglin Rd Luzhou 646000 Sichuan Peoples R China Chengdu Univ Informat Technol Dept Appl Math Chengdu 610225 Sichuan Peoples R China Univ Durham Dept Psychol Durham DH1 3LE England

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

关键词： COVID-19 Dynamic model GA-SEIR model Epidemic forecasting Socio-psychological factors optimization algorithm

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History matching and uncertainty quantification for velocity dependent relative permeability parameters in a gas condensate reservoir

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ARABIAN JOURNAL OF GEOSCIENCES 2019年第15期12卷 454-454页

作者： Farahani, Zohreh Dermanaki Ahmadi, Mohammad Sharifi, Mohammad Amirkabir Univ Technol Hafez Ave 424 Tehran Iran

In gas condensate reservoirs, gas flow at large velocities enhances the gas permeability due to gas-liquid positive coupling which results in near-miscible flow condition. On the other hand, augmented pressure drop due to non-Darcy flow, reduces the gas permeability. Models for the Positive Coupling or non-Darcy flow include several parameters, which are rarely known from reliable lab special core analysis. We offer a good alternative for tuning of these parameters in which the observed production history data are reproduced from the readjusted simulation model. In this study, history matching on observed production data was carried out using evolutionary optimization algorithms including genetic algorithms, neighborhood algorithm, differential evolution algorithm and particle swarm optimization algorithm, where a faster convergence and lower misfit value were obtained from a genetic algorithm. Then, the Neighborhood algorithm-Bayes was used to perform Bayesian posterior inference on the history matched models and create the posterior cumulative probability distributions for all uncertain parameters. Finally, Bayesian credible intervals for production rate and wellhead pressure were computed in the long-range forecast. Our new approach enables to not only calibrate the gas effective permeability parameters to dynamic reservoir data, but allows to capture the uncertainty with parameter estimation and production forecast.

关键词： History matching Uncertainty quantification Velocity dependent relative permeability Gas condensate reservoirs optimization algorithm

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A New Discretization Approach of Bat and K-Means

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第1期12卷 510-516页

作者： Mohamed, Rozlini Samsudin, Noor Azah Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Pant Raja Johor Malaysia

Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

关键词： Classification discretization feature selection optimization algorithm bat algorithm

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Predicting the small strain shear modulus of sands and sand-fines binary mixtures using machine learning algorithms

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TRANSPORTATION GEOTECHNICS 2024年 44卷

作者： Khodkari, Naser Hamidian, Pouria Khodkari, Homayoun Payan, Meghdad Behnood, Ali Univ Tehran Coll Engn Sch Civil Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Turbulence Lab Tehran Iran Univ Guilan Fac Engn Dept Civil Engn Rasht Iran Univ Mississippi Dept Civil Engn Oxford MS USA

This study aims to develop several novel machine learning (ML) evolutionary algorithms for the prediction of small strain shear modulus (Gmax) of clean sands and sand-fines binary mixtures. To this end, five key features of isotropic confining pressure (p), void ratio (e), uniformity coefficient (Cu), particle shape descriptor (rho), and non plastic fines content (FC) are adopted as the inputs to artificial neural network (ANN) models as well as genetic programming (GP) algorithm so as to render the maximum shear modulus of granular soils as the output. Accordingly, a comprehensive dataset containing 1055 Gmax data points is exploited to develop ML simulations. The validity of ML-based models in estimating the Gmax of clean sands and sand-silt mixtures is rigorously examined through various statistical indices and measurement criteria. The results show that a novel ML model utilizing ANN-Levenberg Marquardt (LM) in conjunction with an evolutionary optimization method named Success History-based Adaptive Differential Evolution with Linear population size reduction (LSHADE) is capable of predicting Gmax data with a very high precision rendering R2 values of 0.9833, 0.9841, 0.9802, and 0.9835 for the whole, training, validation, and test datasets, respectively. Meanwhile, using the well-established GP algorithm, a new practical model is proposed to predict the Gmax of clean sands and sand-fines mixtures containing non-cohesive silt inclusion with R2 values of 0.9323, 0.9351, and 0.9312 for the whole, training, and test datasets, respectively. Finally, the proposed models of ANN-LSHADE-LM and GP are shown to be appreciably superior, in terms of accuracy, to all commonly used empirical correlations in the literature for Gmax estimation.

关键词： Small strain shear modulus Artificial neural network (ANN) optimization algorithm Differential evolution (DE) Genetic programming (GP) Sand-silt mixtures

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A mathematical framework to determine the temporal sequence of somatic genetic events in cancer

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2010年第41期107卷 17604-17609页

作者： Attolini, Camille Stephan-Otto Cheng, Yu-Kang Beroukhim, Rameen Getz, Gad Abdel-Wahab, Omar Levine, Ross L. Mellinghoff, Ingo K. Michor, Franziska Mem Sloan Kettering Canc Ctr Computat Biol Ctr New York NY 10065 USA Weill Cornell Med Coll Tri Inst Training Program Computat Biol & Med New York NY 10065 USA Broad Inst Canc Program Cambridge MA 02142 USA Dana Farber Canc Inst Dept Canc Biol Boston MA 02115 USA Dana Farber Canc Inst Dept Med Oncol Boston MA 02115 USA Brigham & Womens Hosp Dept Med Boston MA 02115 USA Harvard Univ Sch Med Dept Med Boston MA 02115 USA Mem Sloan Kettering Canc Ctr Human Oncol & Pathogenesis Program New York NY 10065 USA Mem Sloan Kettering Canc Ctr Dept Hematol New York NY 10065 USA Mem Sloan Kettering Canc Ctr Dept Neurol New York NY 10065 USA

Human cancer is caused by the accumulation of genetic alterations in cells. Of special importance are changes that occur early during malignant transformation because they may result in oncogene addiction and represent promising targets for therapeutic intervention. Here we describe a computational approach, called Retracing the Evolutionary Steps in Cancer (RESIC), to deduce the temporal sequence of genetic events during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage. When applied to a dataset of 70 advanced colorectal cancers, our algorithm accurately predicts the sequence of APC, KRAS, and TP53 mutations previously defined by analyzing tumors at different stages of colon cancer formation. We further validate the method with glioblastoma and leukemia sample data and then apply it to complex integrated genomics databases, finding that high-level EGFR amplification appears to be a late event in primary glioblastomas. RESIC represents the first evolutionary mathematical approach to identify the temporal sequence of mutations driving tumorigenesis and may be useful to guide the validation of candidate genes emerging from cancer genome surveys.

关键词： mathematical modeling stochastic framework optimization algorithm

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INTEGRATION OF GRADIENT LEAST MEAN SQUARES IN BIDIRECTIONAL LONG SHORT-TERM (LSTM) MEMORY NETWORKS FOR METALLURGICAL BEARING BALL FAULT DIAGNOSIS

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METALURGIJA 2024年第3-4期63卷 403-406页

作者： Tang, X. F. Long, Y. B. Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China

This paper introduces a novel diagnostic approach for bearing ball failures: a synergistic implementation of a bidileverages deep analysis of operational data from bearings, enabling the precise identification of incipient bearing ball failures at early stages, thus markedly improving prediction accuracy. Our empirical results underscore the superior performance of this composite methodology in accurately detecting a spectrum of five mechanical bearing ball failure types, achieving a substantial enhancement in diagnostic precision.

关键词： bearing ball fault detection mechanical vibration Bi-LSTM optimization algorithm

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Incorporating stochastics into optimal collision avoidance problems using superquadrics

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Journal of Air Transportation 2020年第2期28卷 65-69页

作者： Smith, Nathan E. Cobb, Richard G. Baker, William P. Air Force Institute of Technology Wright-Patterson Air Force Base U.S. Air Force Ohio45433 United States Department of Aeronautics & Astronautics Air Force Institute of Technology Wright-Patterson Air Force Base Ohio45433 United States Department of Mathematics & Statistics Air Force Institute of Technology Wright-Patterson Air Force Base Ohio45433 United States

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Implementation of a steganography system based on hybrid square quaternion moment compression in IoMT

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第7期35卷

作者： Tahiri, Mohamed Amine Bencherqui, Ahmed Karmouni, Hicham Amakdouf, Hicham Mirjalili, Seyedali Motahhir, Saad Abouhawwash, Mohamed Askar, S. S. Sayyouri, Mhamed Qjidaa, Hassan Sidi Mohamed Ben Abdellah Fez Univ Dhar El Mahrez Fac Sci Lab Elect Signals & Syst Informat LESSI STICCED ST Fes 30000 Morocco Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes 30000 Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech 40000 Morocco Sidi Mohamed Ben Abdellah Univ Inst Sports Sci Fes 30000 Morocco Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Michigan State Univ Coll Engn Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia

Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, med-ical experts, and medical centers over public networks, which require high levels of security to protect the content of medical images and the personal information they contain. In this paper, we propose a new stego image encryption scheme based on a new secret image compression method, wavelet trans-formation, QR decomposition of the cover image, and a new chaotic map. The secret image is compressed by the Hahn-Krawtchouk hybrid quaternion square moments (HK-HQSM), which are optimized by a new hybrid metaheuristic algorithm based on the Salp Swarm algorithm (SSA) and the Arithmetic optimization algorithm (AOA). To increase the security level when transmitting the proposed stego im-ages over public networks, we introduce a new chaotic map based on the 2D fractional Henon map to encrypt the stego image. To demonstrate the effectiveness of the proposed steganography scheme for IoMT, we implemented this scheme on a low-cost Raspberry Pi 4 hardware board. The results of the per-formed numerical experiments show that our method is secure and provides exceptional robustness against common standard image processing attacks (steganalysis attacks). The results also demonstrate that our strategy is able to work efficiently and quickly when implemented on a Raspberry Pi board.& COPY;2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Index Terms Image steganography Internet of Medical Things Hybrid square moments optimization algorithm Raspberry Pi

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DNA algorithmS BASED ON EXON SHUFFLING

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IFAC Proceedings Volumes 2005年第1期38卷 17-21页

作者： Tsuyoshi Okayama Haruhiko Murase Osaka Prefecture University 1-1 Gakuen Sakai 599-8531 Japan

An understanding of a natural system's information handling can lead to more effective artificial optimization techniques. There are successful optimization algorithms represented in biosystems that have proven useful in engineering applications (artificial neural networks, immune system algorithms, etc). The goal of our study is to develop a new biosystem derived an optimization algorithm which is called a DNA algorithm (DNAA) based on optimization procedures in DNA. We have focused on an analogy between optimizing procedures for protein functions using exon shuffling and those for an optimization problem in the engineering field. We used a traveling salesman problem (TSP) for evaluation of the performance of the DNAA. The DNAA could estimate approximately optimal tour routes in the 25-city TSP.

关键词： intron exon exon shuffling optimization algorithm traveling salesman problem

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