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Simultaneous structural and operating optimization of process flowsheets combining process simulators and metaheuristic techniques: The case of solar-grade silicon process

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COMPUTERS & CHEMICAL ENGINEERING 2020年 140卷 106946-106946页

作者： Hernandez-Perez, Luis G. Ramirez-Marquez, Cesar Segovia-Hernandez, Juan G. Ponce-Ortega, Jose M. Univ Michoacana Chem Engn Dept Francisco J Mujica S-NEdificio V1Ciudad Univ Morelia 58060 Michoacan Mexico Univ Guanajuato Dept Ingn Quim Div Ciencias Nat & Exactas Campus GuanajuatoNoria Alta S-N Guanajuatogto 20256 Mexico

This paper presents a new optimization approach for the simultaneous structural optimization of process flowsheets with the operating conditions through combining process simulators with metaheuristic tech-niques. The proposed approach allows optimization of a superstructure in process simulators and reduce the computation time. A superstructure for different configurations for producing solar-grade silicon is considered, which includes three different configurations for solar-grade silicon production (Siemens Pro-cess, Intensified FBR Union Carbide Process, and Hybrid Process). The operating conditions with major impact in the performance of each of the proposed configuration were considered as decision variables. The improved multi-objective differential evolution (I-MODE) algorithm was selected as search method from others metaheuristic techniques because its efficiency to solve multi-objective problems in a short central process unit (CPU) time. The optimization algorithm consists in linking the process simulator software Aspen PlusTM with the metaheuristic technique. The results offered attractive options for the considered objective functions in the addressed case study. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Structural optimization through process simulators Optimal selection and design Sola-grade silicon production process metaheuristic optimization algorithm Simultaneous simulation in multi-case configuration process

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A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and metaheuristic Global optimization algorithms

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ENERGIES 2020年第4期13卷 801-801页

作者： Perez Archila, Luis Miguel David Bastidas-Rodriguez, Juan Correa, Rodrigo Trejos Grisales, Luz Adriana Gonzalez-Montoya, Daniel Univ Ind Santander Escuela Ingn Elect Elect & Telecomunicac Bucaramanga 680003 Colombia Univ Nacl Colombia Dept Ingn Elect Elect & Comp Manizales 170003 Colombia Inst Tecnol Metropolitano Dept Electromecan & Mecatron Medellin 050013 Colombia Inst Tecnol Metropolitano Dept Elect & Telecomunicac Medellin 050013 Colombia

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current-voltage (I-V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I-V curves in all the cases;nevertheless, the two metaheuristic optimization methods only reproduce the I-V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.

关键词： implicit model solution photovoltaic array series-parallel global optimization partial shading deterministic optimization algorithm metaheuristic optimization algorithm

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A new evolutionary algorithm: Learner performance based behavior algorithm

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EGYPTIAN INFORMATICS JOURNAL 2021年第2期22卷 213-223页

作者： Rahman, Chnoor M. Rashid, Tarik A. Charmo Univ Coll Med & Appl Sci Appl Comp Dept Sulaimany Iraq Sulaimany Polytech Univ Tech Coll Informat Sulaimany Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization;exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner's studying behavior at university to improve the level of their study, respectively. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.

关键词： Evolutionary algorithms Genetic algorithm LPB Learner performance based behavior algorithm optimization metaheuristic optimization algorithm

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Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2023年第2期13卷 661-689页

作者： Sheykhinasab, Amirhossein Mohseni, Amir Ali Bahari, Arash Barahooie Naruei, Ehsan Davoodi, Shadfar Aghaz, Aliakbar Mehrad, Mohammad ACECR Inst Higher Educ Isfahan Branch Esfahan Iran Islamic Azad Univ Dept Oil & Chem Engn Sci & Res Branch Tehran Iran Lamerd Higher Educ Ctr Petr Engn Dept Lamerd Fars Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Sahand Univ Technol Fac Oil & Gas Engn Sahand Oil & Gas Res Inst Tabriz Iran Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahrood Iran

Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R-2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R-2 shed light on the MELM-COA as the best-performing model in the training and testing stage

关键词： Prediction of permeability Heterogeneous carbonate reservoir Hybrid prediction model metaheuristic optimization algorithm Deep learning

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An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting

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ACTA GEOPHYSICA 2024年第5期72卷 3661-3681页

作者： Kilinc, Huseyin Cagan Haznedar, Bulent Ozkan, Furkan Katipoglu, Okan Mert Istanbul Aydin Univ Dept Civil Engn Istanbul Turkiye Gaziantep Univ Dept Comp Engn Gaziantep Turkiye Cukurova Univ Dept Comp Engn Adana Turkiye Erzincan Binali Yildirim Univ Dept Civil Engn Erzincan Turkiye

Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting the ecological health of the watershed. This study presents a comprehensive analysis of river flow data in Turkey's Meric basin using long-term daily data spanning the years 2001-2011. Within the scope of forecasting of daily river flows, 80% of dataset was used for training stage and the 20% of dataset was used for testing. The performance of four different models, including Extreme Gradient Boosting (XGBoost) benchmark model, Linear Regression, Long Short-Term Memory Network (LSTM), and proposed Particle Swarm optimization (PSO)-XGBoost hybrid model, was evaluated using several statistical evaluation criteria such as RMSE, MAPE, MAE, SD, NSE, KGE, and model success was assessed based on determination coefficient (R2) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R2 values of 0.7460 at the Babaeski FMS station, 0.9582 at the Meric FMS station, and 0.8116 at the Hayrabolu FMS station, respectively. It has been found that when the parameters of the XGBoost model are optimized with the meta-heuristic PSO algorithm, the daily flow prediction accuracy is significantly increased in the Meric River basin, which has a pluviozonal River regime. The results demonstrate that the proposed hybrid model produces precise and reliable estimations for daily river flow analysis in the Meric basin. The findings derived from the proposed model demonstrate its promising potential for water resource management and decision-making in the region.

关键词： Proposed model Prediction Machine learning metaheuristic optimization algorithm Streamflow

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A Generalized Framework for Multi-objective-based Constraint Handling Technique

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第10期22卷 3240-3252页

作者： Dam, Tien Minh Truong, Long Viet Viettel High Technol Ind Corp Subsidiary Viettel Grp Hanoi 10000 Vietnam

This paper introduces a novel approach to handling constraints in multi-objective optimization problems by treating constraints as objectives. Our study consists of three main contributions. First, we present a generalized framework for multi-objective-based constraint handling techniques, which integrates multi-objective-based methods with the constraint dominance principle. This framework systematically incorporates various aspects of constraint violations into the objective function, enhancing optimization strategies. Second, we propose the constraint violation ratio, a metric that uses a constraint weight vector to quantify the severity of violations. Third, we develop the constraint diversity factor, an adaptive version of the constraint weight vector, which adapts as the frequency of constraint violations changes. Extensive experimental evaluations validate the effectiveness of our methods, demonstrating their applicability across a wide range of constrained optimization scenarios.

关键词： Constrained multi-objective evolutionary algorithm constrained multi-objective optimization problem constraint as objectives constraint handling techniques generalized framework for constraint handling metaheuristic optimization algorithm

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Parameters and methods used in flood susceptibility mapping: a review

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JOURNAL OF WATER AND CLIMATE CHANGE 2023年第6期14卷 1935-1960页

作者： Kaya, Cagla Melisa Derin, Leyla IKCU Dept Geog Izmir Turkiye Ankara Univ Emergency & Disaster Management Program Ankara Turkiye

A correct understanding of the parameters and methods used in flood susceptibility mapping (FSM) is critical for identifying the strengths and limitations of different mapping approaches, as well as for developing methodologies. In this study, we examined scientific publications in the literature using WoS. Although the number of methods used is quite high (about 160 with submethods), the number of parameters used in these methods varies, with a maximum of 21 and a minimum of 5 parameters preferred. It was found that the most commonly used parameter has a preference rate of 97%, but there is no common parameter in 100% of the studies. The methods used for determining flood susceptibility include multi-criteria decision-making (MCDM) methods, physically based hydrological models, statistical methods, and various soft computing methods. Although the use of traditional statistical methods and MCDM methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning. In the reviewed studies, it was observed that machine learning, fuzzy logic, metaheuristic optimization algorithms, and heuristic search algorithms, which are soft computing methods, have been widely used in FSM in recent years.

关键词： flood susceptibility mapping (FSM) metaheuristic optimization algorithm parameters statistical methods

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A hybrid encryption model for the hyperspectral images: application to hyperspectral medical images

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第4期83卷 11717-11743页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet St Longowal Inst Engn & Technol Dept Comp Sci & Engn Longowal 148106 Punjab India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India

Hyperspectral images collect information across the electromagnetic spectrum and are widely used to recognize signals, identify materials, and find objects. However, the information in a hyperspectral image may be sensitive and must be protected. Therefore, this paper proposes a new information confidentiality model for hyperspectral images. In this model, Self Adaptive Bald Eagle Search (SABES) optimization algorithm is proposed to select the initial and control parameters of the chaos maps to improve the encryption process. A multi-level chaotic system is implemented to enhance the security of the hyperspectral images by increasing the randomness. A circular shift is utilized to secure the proposed model from differential attack. The proposed encryption model is authenticated with various security analyses. The experimental results show that the proposed cryptographic model is secure from different attacks (statistical, differential, noise, and cropping). The performance of the proposed model is validated by comparing the results of the proposed model with the state-of-the-art methods. The results prove that the proposed model is more secure and requires less computational time for image encryption and decryption in comparison to the existing methods. In addition, the proposed encryption model is applied to secure the hyperspectral medical images, demonstrating its utility in the medical field.

关键词： Chaotic maps Decryption Encryption Hyperspectral images metaheuristic optimization algorithm

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Boosting galactic swarm optimization with ABC

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第9期10卷 2401-2419页

作者： Kaya, Ersin Uymaz, Sait Ali Kocer, Baris Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn Konya Turkey

Galactic swarm optimization (GSO) is a new global metaheuristic optimization algorithm. It manages multiple sub-populations to explore search space efficiently. Then superswarm is recruited from the best-found solutions. Actually, GSO is a framework. In this framework, search method in both sub-population and superswarm can be selected differently. In the original work, particle swarm optimization is used as the search method in both phases. In this work, performance of the state of the art and well known methods are tested under GSO framework. Experiments show that performance of artificial bee colony algorithm under the GSO framework is the best among the other algorithms both under GSO framework and original algorithms.

关键词： Galactic swarm optimization Artificial bee colony algorithm Swarm intelligence metaheuristic optimization algorithm

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