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Hydrate formation prediction model for binary gases of methane plus ethane and methane plus propane by using optimization algorithm

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PETROLEUM SCIENCE AND TECHNOLOGY 2020年第1期38卷 36-42页

作者： Abbasi, Aijaz Hashim, Fakhruldin Mohd Univ Teknol PETRONAS Mech Engn Dept Seri Iskandar Malaysia

This research model equation is based on constant coefficients which were used for multiple sets of experimental data. The developed optimized based prediction model is examined by the experimental data of binary gases methane + ethane and methane + propane hydrates, respectively. This research model holds a wide range of datasets from 190.2 to 326.8 K and 0.08251 to 590 MPa for binary gases of methane + ethane and methane + propane, respectively. The developed algorithm validated by experimental data of binary gases of hydrate formation, statistical analysis, thermodynamic equations and fundamental empirical models for hydrate formation, respectively. This research enhances the existing study by presenting a systematic model which predicts and provides a guideline for users to forecast gas-hydrate-forming conditions.

关键词： pressure temperature statistical analyses thermodynamics hydrate formation model optimization algorithms

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Application of the arithmetic optimization algorithm to solve the optimal power flow problem in direct current networks

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RESULTS IN ENGINEERING 2022年 16卷

作者： Montano, Jhon Daniel Garzon, Oscar Rosales Munoz, Andres Alfonso Grisales-Norena, L. F. Danilo Montoya, Oscar Inst Tecnol Metropolitano Dept Elect & Telecommun Medellin 050028 Colombia Univ Puerto Rico Mayaguez Elect & Comp Engn Dept Mayaguez PR 00680 USA Inst Tecnol Metropolitano Fac Ingn Grp MATyER Campus Robledo Medellin 050036 Colombia Univ Dist Francisco Jose de Caldas Fac Ingn Carrera 7 40B-53 Bogota 11021 Colombia Univ Tecnol Bolivar Lab Inteligente Energia Km 1 Via Turbaco Cartagena 131001 Colombia

This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic optimization Algorithm (AOA) and Successive Approximation (SA). This master -slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented;thus, the methodology explores so-lutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion optimization (ALO), Black Hole optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation.

关键词： Optimal power flow Power flow optimization algorithms DC networks Electric power optimization

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Exploring the Communication Path of Civic Education in Cyberspace Using Data Mining Technology

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Li, Sha Party Committee Organization Department Wuxi Vocational Institute of Commerce Jiangsu Wuxi214153 China

A recommendation algorithm is a specific application of data mining technology, which is an important means to expand the dissemination of Civics education in cyberspace. The elbow method is employed in this paper to determine the best number of clusters to cluster the Civics course evaluation dataset and finish preprocessing large-scale data. Aiming at the problem of different importance of rating bias between items, the traditional Slope One algorithm is optimized by comprehensively considering the relationship between the number of common ratings and the similarity between items and the prediction results. The optimized Slope One algorithm is used to perform one round of prediction and fill the scoring matrix to solve the data sparsity problem and improve prediction accuracy and stability. The optimized algorithm reduces by 8.56% compared to the user-based collaborative filtering algorithm, 5.83% compared to the item-based collaborative filtering algorithm, and 5.25% compared to the content-based recommendation algorithm, with a significant improvement in accuracy, recall, and F1 value. The four hypothesis validations indicate that the diversity, personalization, and accuracy of resource recommendations all have positive effects on the dissemination effect of civic education. The recommendation algorithm's dissemination effect score in this paper is 3.781, which is significantly superior to the dissemination effect of Civic Education. © 2024 Sha Li, published by Sciendo.

关键词： optimization algorithms

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Optimizing Hyperspectral Image Classification Through Advanced Deep Learning Models Enhanced by Adam

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TRAITEMENT DU SIGNAL 2024年第2期41卷 867-877页

作者： Sundararajan, Manju Selvam, Sinthia Panneer Velammal Engn Coll Dept ECE Surapet 600066 India Saveetha Engn Coll Dept BME Thandalam 602105 India

In modern urban planning, agricultural production, and environmental monitoring, classification tasks necessitate sophisticated approaches due to the inherent complexity of hyperspectral images (HSIs), characterized by their abundant spectral bands and consequent high dimensionality. Such profusion poses significant challenges for effective data processing, analysis, and classification. Addressing these challenges, the application of deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a pivotal advancement. By exploiting the inherent spectral correlation and spatial context, these networks are adept at extracting pertinent features from the high-dimensional data of HSIs, thereby significantly enhancing classification performance. This study introduces four novel deep learning models optimized with the Adam algorithm: a 3-Dimensional Convolutional Neural Network (3D-CNN), a 2-Dimensional Convolutional Neural Network (2D-CNN), a recurrent 3D-CNN (R-3D CNN), and a recurrent 2D-CNN (R-2D-CNN). The Adam optimizer, known for its adaptive nature and the utilization of moving averages of gradients and their squares, is demonstrated to efficiently handle sparse gradients, thus providing stability during the optimization process. Comprehensive analyses were conducted on two publicly available databases-Indian Pine and Pavia University-yielding notable results. The employment of the Adam optimizer facilitated the attainment of exceptional performance metrics, evidenced by a kappa coefficient of 99.6%, a processing time of 322.18 seconds, an overall accuracy of 99.97%, and an average accuracy of 97.6% for the Indian Pine dataset;similarly, for the Pavia University dataset, results showcased a kappa coefficient of 99.1%, a processing time of 293.74 seconds, an overall accuracy of 99.98%, and an average accuracy of 99.99%. These findings underscore the superiority of the proposed deep learning models, particularly the R-3D-CNN and R-2D-CNN, over tradi

关键词： deep learning hyperspectral images spatial context spectral correlation feature extraction optimization algorithms convolutional neural network (CNN) Adam optimizer

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The Effectiveness of the Application of Intelligent Aids in the Blended Teaching of College English under the Perspective of New Liberal Arts Education

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Chao, Weihong School of Foreign Languages Zhengzhou University of Industrial Technology Henan Zhengzhou451100 China

The integration and promotion of intelligent technology in the education industry has led to the introduction of more and more intelligent assistive teaching tools and platforms into the classroom, and the traditional classroom mode and teaching form are undergoing significant changes. This paper utilizes a university teaching and quality evaluation system to design the teaching evaluation module. Improving the particle swarm optimization algorithm and combining it with the K-Means algorithm has resulted in the construction of a teaching evaluation model based on the PSKA algorithm. The model is incorporated into the English teaching and quality evaluation system in colleges and universities. The PSKA algorithm is used to cluster students' learning situations and divide four categories of students with different learning levels. Targeted guidance for each category of students' learning problems can effectively improve the quality of teaching. The questionnaire survey revealed that 61.07% of English teachers understood the model, with 47.25% saying they actively use it and recommend it to other teachers. It is evident that the teaching evaluation model based on the PSKA algorithm is recognized by most English teachers. © 2024 Weihong Chao., published by Sciendo.

关键词： optimization algorithms

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A sociologically inspired heuristic for optimization algorithms: A case study on ant systems

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第5期40卷 1814-1826页

作者： Ribeiro, Richardson Enembreck, Fabricio Fed Technol Univ Parana UTFPR Dept Informat COINF Res Grp Commun & Informat Technol GETIC Pato Branco Brazil Pontifical Catholic Univ Parana PUCPR Postgrad Program Informat PPGIa Curitiba Parana Brazil

This paper discusses how social network theory can provide optimization algorithms with social heuristics. The foundations of this approach were used in the SAnt-Q (Social Ant-Q) algorithm, which combines theory from different fields to build social structures for state-space search, in terms of the ways that interactions between states occur and reinforcements are generated. Social measures are therefore used as a heuristic to guide exploration and approximation processes. Trial and error optimization techniques are based on reinforcements and are often used to improve behavior and coordination between individuals in a multi-agent system, although without guarantees of convergence in the short term. Experiments show that identifying different social behavior within the social structure that incorporates patterns of occurrence between states explored helps to improve ant coordination and optimization process within Ant-Q and SAnt-Q giving better results that are statistically significant. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Ant systems Collective behavior Social structure Social network theory optimization algorithms

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Proportional-Integral Projected Gradient Method for Model Predictive Control

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IEEE CONTROL SYSTEMS LETTERS 2021年第6期5卷 2174-2179页

作者： Yu, Yue Elango, Purnanand Acikmese, Behcet Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA

Recently there has been an increasing interest in primal-dual methods for model predictive control (MPC), which require minimizing the (augmented) Lagrangian at each iteration. We propose a novel first order primaldual method, termed proportional-integral projected gradient method, for MPC where the underlying finite horizon optimal control problem has both state and input constraints. Instead of minimizing the (augmented) Lagrangian, each iteration of our method only computes a single projection onto the state and input constraint set. Our method ensures that, along a sequence of averaged iterates, both the distance to optimum and the constraint violation converge to zero at a rate of O(1/k) if the objective function is convex, where k is the iteration number. If the objective function is strongly convex, this rate can be improved to O(1/k(2)) for the distance to optimum and O(1/k(3)) for the constraint violation. We compare our method against existing methods via a trajectory-planning example with convexified keep-out-zone constraints.

关键词： Convergence Gradient methods Predictive control Minimization Symmetric matrices Optimal control Matrix decomposition Model predictive control optimization algorithms

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Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 6925-6938页

作者： Tharwat, Alaa Gabel, Thomas Frankfurt Univ Appl Sci Fac Comp Sci & Engn Frankfurt Germany

The parameters of support vector machines (SVMs) such as kernel parameters and the penalty parameter have a great influence on the accuracy and complexity of the classification models. In the past, different evolutionary optimization algorithms were employed for optimizing SVMs;in this paper, we propose a social ski-driver (SSD) optimization algorithm which is inspired from different evolutionary optimization algorithms for optimizing the parameters of SVMs, with the aim of improving the classification performance. To cope with the problem of imbalanced data which is one of the challenging problems for building robust classification models, the proposed algorithm (SSD-SVM) was enhanced to deal with imbalanced data. In this study, eight standard imbalanced datasets were used for testing our proposed algorithm. For verification, the results of the SSD-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and particle swarm optimization (PSO). The experimental results show that the SSD-SVM algorithm is capable of finding near-optimal values of SVMs parameters. The results also demonstrated high classification performance compared to the PSO algorithm.

关键词： optimization algorithms Support vector machine (SVM) Parameter optimization Imbalanced data

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Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Tao, Hai Abba, Sani I. Al-Areeq, Ahmed M. Tangang, Fredolin Samantaray, Sandeep Sahoo, Abinash Siqueira, Hugo Valadares Maroufpoor, Saman Demir, Vahdettin Bokde, Neeraj Dhanraj Goliatt, Leonardo Jamei, Mehdi Ahmadianfar, Iman Bhagat, Suraj Kumar Halder, Bijay Guo, Tianli Helman, Daniel S. Ali, Mumtaz Sattar, Sabaa Al-Khafaji, Zainab Shahid, Shamsuddin Yaseen, Zaher Mundher Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Qiannan Normal Univ Nat Inst Big Data Applicat & Artificial Intelligence Duyun 558000 Guizhou Peoples R China INTI Int Univ Fac Data Sci & Informat Technol Nilai Malaysia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Membrane & Water Secur Dhahran 31261 Saudi Arabia Univ Kebangsaan Malaysia Fac Sci & Technol Dept Earth Sci & Environm Bangi 43600 Selangor Malaysia Natl Inst Technol Dept Civil Engn Srinagar 190006 J&k India Odisha Univ Technol & Res Dept Civil Engn Bhubaneswar Orissa India Univ Tecnol Fed Parana Grad Program Comp Sci Grad Program Ind Engn Campus Ponta Grossa Curitiba PR Brazil Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran KTO Karatay Univ Dept Civil Engn TR-42020 Konya Turkiye Aarhus Univ Ctr Quantitat Genet & Genom DK-8000 Aarhus Denmark Univ Fed Juiz de Fora Dept Computat & Appl Mech BR-36036900 Juiz De Fora MG Brazil Shahid Chamran Univ Ahvaz Fac Civil Engn & Architecture Dept Civil Engn Ahvaz Iran Univ Prince Edward Isl Canadian Ctr Climate Change & Adaptat St Peters PE Canada Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Nasiriyah 64001 Thi Qar Iraq Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Coll Micronesia FSM Educ Div Yap Campus Ponape Micronesia Univ Southern Queensland UniSQ Coll Toowoomba Qld 4350 Australia Al Mustaqbal Univ Bldg & Construct Tech Engn Dept Hillah 51001 Babil Iraq Al Turath Univ Coll Baghdad Iraq Univ Teknol Malaysia Dept Water & Environm Engn Johor Baharu 81310 Malaysia King Fahd Univ Petr & Minerals Civil & Environm Engn Dept Dhahran 31261 Saudi Arabia

River flow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflow modeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000-2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input pa-rameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication.

关键词： Data availability Machine learning Nature-inspired algorithms River flow modeling optimization algorithms

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A committee machine-based estimation of shear velocity log by combining intelligent systems and rock-physics model using metaheuristic algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期126卷

作者： Nasrnia, Behzad Falahat, Reza Kadkhodaie, Ali Vijouyeh, Ali Gholami Sahand Univ Technol Sahand Iran Tabriz Univ Tabriz Iran

Estimation of reservoir parameters such as porosity, lithology and determination of elastic moduli is based on well-logging data and seismic velocity information. However, the lack of shear velocity log in many wells, and the high costs of its acquisition have led to developing different approaches for shear velocity estimation. Currently, shear velocity estimation is accomplished by common industrial methods such as empirical equations, regression analysis, intelligent methods and rock-physics models (RPMs). The current study proposes that robust, accurate and cost-effective intelligent methods coupled with suitable RPM provide reasonable estimations of the shear velocity in an optimal processing time. Hybridizing intelligent models with RPMs in the framework of a committee machine is an optimal solution for shear velocity estimation of carbonate reservoirs. The intelligent methods used in this study include neural network (NN), fuzzy logic (FL) and an adaptive-neuro fuzzy inference system (ANFIS). In this research, for each individual intelligent model and a developed RPM, the optimal weights were allocated by Metaheuristic optimization algorithms including genetic algorithm (GA), ant colony optimization routing (ACOR) and simulated annealing (SA)). To obtain the best results with the lowest mean squared error (MSE), a total of 9408, 10801 and 12288 models were created and tested in MATLAB to optimally parametrize the GA, ACOR and SA, respectively. The results showed that the combination of the applied intelligent models with the developed RPMs leads to a higher correlation coefficient with reference to the measured Shear velocity in test dataset. The use of a combination of intelligent methods in the form of committee-machine instead of individual methods has led to a reduction in error and improved the results by up to 2% Considering the high sensitivity of shear wave velocity in geomechanical and reservoir characterization studies, even small improvements in it

关键词： Shear velocity Neural network Fuzzy logic Neuro-fuzzy optimization algorithms Rock-physics model Committee machine

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