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A comparison study of using optimization algorithms and artificial neural networks for predicting permeability

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2013年 112卷 17-23页

作者： Kaydani, Hossein Mohebbi, Ali Shahid Bahonar Univ Kerman Coll Engn Dept Chem Engn Kerman Iran

This paper presents a novel approach of permeability prediction by combining cuckoo, particle swarm and imperialist competitive algorithms with Levenberg-Marquardt (LM) neural network algorithm in one of heterogeneous oil reservoirs in Iran. First, topology and parameters of the Artificial Neural Network (ANN) as decision variables were designed without the optimization method. Then, in order to improve the effectiveness of forecasting when ANN was applied to a permeability predicting problem, the design was performed using cuckoo optimization algorithm (COA) algorithm. The validation test result from a new well data demonstrated that the trained COA-LM neural model can efficiently accomplish permeability prediction. Also, the comparison of COA with particle swarm optimization and imperialist competitive algorithms showed the superiority of COA on fast convergence and best optimum solution achievement. (C) 2013 Elsevier B.V. All rights reserved.

关键词： permeability neural network cuckoo optimization algorithm particle swarm optimization imperialist competitive algorithm well logs data

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Prediction of the viscosity of water-in-oil emulsions

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第24期34卷 1972-1977页

作者： Nasery, Saeid Hoseinpour, Seyedahmad Le Thi Kim Phung Bahadori, Alireza Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran Petr Univ Technol Dept Petr Engn Ahvaz Iran Hochiminh City Univ Technol Dept Chem Proc & Equipment Fac Chem Engn Hochiminh City Vietnam Southern Cross Univ Sch Environm Sci & Engn POB 157 Lismore NSW Australia Australian Oil & Gas Serv Pty Ltd Lismore Australia

Water-in-oil (W/O) emulsions occur in different parts of petroleum recovery and since their properties are different from those of crude oil and water, it is essential to find information about their physical properties. The water causes some problems and heightens the cost of oil production. Separation of water from oil, water treatment, and disposal of water are the steps that make oil production costly. Among various physical properties of W/O emulsions, viscosity is the most important. Because of droplet crowding, emulsions indicate non-Newtonian behavior, which is why their viscosity is higher than the viscosity of oil and water. As a result, it is essential to have knowledge about the viscosity of W/O emulsions in order to extract and process petroleum properly. In this study, an intelligent model, namely a COA-LSSVM, is presented to accurately prognosticate the viscosity of W/O emulsions. The results indicated that the values estimated by the developed model is in great consistency with the laboratory data by R-2 of 0.9972 and MSE of 1.1762 * 10(-5). A comparison with another model, which was recently introduced, revealed that the developed model in this study is superior.

关键词： cuckoo optimization algorithm least square support vector machine viscosity of water-oil emulsion water-oil emulsion

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Estimation of the compressive strength of 28-day-old concrete by use of an adaptive cuckoo-fuzzy logic model

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RESEARCH ON CHEMICAL INTERMEDIATES 2013年第9期39卷 4001-4009页

作者： Abolpour, Bahador Mohebbi, Ali Shahid Bahonar Univ Kerman Fac Engn Dept Chem Engn Kerman Iran

In this study, the cuckoo optimization algorithm was used to design a fuzzy logic model for determination of the compressive strength of 28-day-old concrete. Experimental results from 50 concrete mixtures were loaded into the fuzzy logic model for training, and the model was then optimized by use of the cuckoo optimization algorithm. Input variables of the fuzzy logic model are water-to-cement weight ratio and coarse aggregate-to-fine aggregate weight ratio;the output variable is concrete compressive strength. The results obtained from the optimized model were compared with those from an adaptive neuro fuzzy inference system model;they were also validated experimentally.

关键词： Concrete mixture Compressive strength cuckoo optimization algorithm Fuzzy logic Modeling

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Inspection of textile fabrics based on the optimal Gabor filter

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SIGNAL IMAGE AND VIDEO PROCESSING 2021年第7期15卷 1617-1625页

作者： Boluki, Mina Mohanna, Farahnaz Univ Sistan & Baluchestan Dept Commun Engn Zahedan Iran

Defect detection and quality control play an important role in the textile industry. In this paper, an automatic algorithm based on the optimal Gabor filter is proposed for real-time inspection of textile fabrics. The cuckoo optimization algorithm is adopted to optimize the parameters of the Gabor filter. Also, an adaptive local binarization method is proposed, which enhances the performance of our algorithm. In order to locate the defects, the filtered image is divided into non-overlapping blocks. Then, the candidate defective blocks are binarized using adaptive thresholds, which are determined by blocks statistics. The performance of the proposed algorithm is evaluated through different types of fabrics in the TILDA database and an online Fabric Stain Dataset. The experimental results demonstrate the efficiency of the proposed method in detecting defects on the plain, regular and irregular patterned fabrics. Furthermore, the comparative results are provided to show the robustness of the proposed method.

关键词： Textile quality control Fabric defect detection Optimal Gabor filter cuckoo optimization algorithm Segmentation

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A comprehensive study of cuckoo-inspired algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第2期29卷 345-361页

作者： Abdel-Basset, Mohamed Hessin, Abdel-Naser Abdel-Fatah, Lila Zagazig Univ Fac Comp & Informat Zagazig Egypt

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them.

关键词： Metaheuristic cuckoo search cuckoo optimization algorithm

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Optimal replacement strategy of obsolete industrial components under fuzzy data

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2020年第3期234卷 349-357页

作者： Mellal, Mohamed Arezki Williams, Edward J. MHamed Bougara Univ Fac Engn Sci FSI LMSS Boumerdes 35000 Algeria Univ Michigan Coll Engn & Comp Sci Ind & Mfg Syst Engn Dept Dearborn MI 48128 USA Univ Michigan Coll Business Decis Sci Dearborn MI 48128 USA

Competitiveness and rapid technological advances lead to the obsolescence of household items and industrial components. The challenge to people and industrial firms is to be equipped with the latest items, but at the lowest cost. During the last decade, several works have investigated various replacement strategies in order to optimally replace the obsolete industrial components. This article introduces the first work of the literature addressing a replacement strategy in case of fuzzy data on the components. The approach is based on fuzzy logic and the cuckoo optimization algorithm. Two case studies are illustrated for highlighting the applicability of the approach proposed.

关键词： Obsolescence replacement strategy fuzzy logic cuckoo optimization algorithm

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Comparison of LLNF, ANN, and COA-ANN Techniques in Modeling the Uniaxial Compressive Strength and Static Young's Modulus of Limestone of the Dalan Formation

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NATURAL RESOURCES RESEARCH 2019年第1期28卷 223-239页

作者： Mokhtari, Maryam Behnia, Mahmoud Yazd Univ Dept Civil Engn Fac Engn POB 89195-741 Yazd Iran Isfahan Univ Technol Dept Min Engn Esfahan *** Iran

The uniaxial compressive strength and static Young's modulus are among the key design parameters typically used in geotechnical engineering projects. In this paper, three artificial intelligence techniques, namely the local linear neuro-fuzzy (LLNF) technique, artificial neural network (ANN) and the hybrid cuckoo optimization algorithm-artificial neural network (COA-ANN), were used to estimate the uniaxial compressive strength and the static Young's modulus of limestone. For this purpose, 115 limestone samples were subjected to the tests of uniaxial compressive strength, ultrasonic velocity, and physical properties (density and porosity) tests. From the laboratory results obtained, the values of the P-wave velocity, density, porosity and dynamic Poisson's ratio were tested as the model input parameters to determine the best input configuration for estimating the uniaxial compressive strength and the static Young's modulus. Different models with different input combinations were practiced, and the models with the highest estimation accuracy are reported here. Performance evaluation was carried out using three criteria including coefficient of determination, variance accounted for, and normalized mean-square error. Evaluating the correlation coefficients and error criteria resulting from the three methods used demonstrates the superiority of LLNF method to ANN and COA-ANN methods. The developed ANN models display lower correlation coefficients and higher amount of error compared to the other models. However, using cuckoo optimization algorithm has led to significant improvement in accuracy and precision of estimations carried out by ANN and has improved its efficiency. Results have confirmed that the employed hybrid method outperforms in estimating untrained data (test data) compared to the LLNF and ANN methods.

关键词： Uniaxial compressive strength Static Young's modulus Ultrasonic velocity Local linear neuro-fuzzy Artificial neural network cuckoo optimization algorithm

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Stability enhancement of hybrid electric vehicles using optimal fuzzy logic

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第4期12卷 1130页

作者： Muhammad, Ebrahim Vali, Ahmadreza Kashaninia, Abdoreza Bahnamgol, Vahid Malek Ashter Univ Technol Dept Elect & Comp Engn Tehran Iran

This paper investigates a fuzzy control method based on the cuckoo optimization algorithm (COA) strategy to enhance the lateral dynamics of the vehicle under uncertain vehicle model parameters. The direct yaw control system has a two-layer control structure. A corrective yaw moment (M-z) is obtained in the upper layer by optimizing the fuzzy controller's input-output weights and membership functions based on COA. In the lower layer, the corrective yaw moment is converted into braking torque, which is applied to the left or right rear wheels depending on the distribution algorithm. Simulations of three scenarios under various road conditions were conducted to ensure the robustness of the proposed control strategy against changes in road conditions. A nonlinear 8-DoF dynamic model for the vehicle with a Dugoff tire model is used for computer simulations on MATLAB. Simulation results during a single lane change and J-turn maneuvers on a dry and rainy road compared the performance of the suggested optimal fuzzy controller with the non-optimal fuzzy controller. The simulation results showed that the proposed control system improved the stability and handling of the system's tracking performance in various maneuvers.

关键词： Vehicle lateral stability Direct yaw control Fuzzy control cuckoo optimization algorithm

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The Effect of Plug-in Electric Vehicles on Harmonic Analysis of Smart Grid

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2015年第6期16卷 559-567页

作者： Heidarian, T. Joorabian, M. Reza, A. Ahvaz Elect Power Distribut Co Ahvaz Iran Shahid Chamran Univ Ahvaz Dept Elect Engn Ahvaz Iran Islamic Azad Univ Dept Mech Engn Ahvaz Branch Ahvaz Iran

In this paper, the effect of plug-in electric vehicles is studied on the smart distribution system with a standard IEEE 30-bus network. At first, harmonic power flow analysis is performed by Newton-Raphson method and by considering distorted substation voltage. Afterward, proper sizes of capacitors is selected by cuckoo optimization algorithm to reduce the power losses and cost and by imposing acceptable limit for total harmonic distortion and RMS voltages. It is proposed that the impact of generated current harmonics by electric vehicle battery chargers should be factored into overall load control strategies of smart appliances. This study is generalized to the different hours of a day by using daily load curve, and then optimum time for charging of electric vehicles batteries in the parking lots are determined by cuckoo optimization algorithm. The results show that injecting harmonic currents of plug-in electric vehicles causes a drop in the voltage profile and increases power loss. Moreover, charging the vehicle batteries has more impact on increasing the power losses rather than the harmonic currents effect. Also, the findings showed that the current harmonics has a great influence on increasing of THD. Finally, optimum working times of all parking lots was obtained for the utilization cost reduction.

关键词： harmonic power flow capacitor sizing electric vehicle cuckoo optimization algorithm distribution network smart grid

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Optimal Operation and Output Oscillations Reduction of PEMFC by using an Intelligent Strategy

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2014年第8期9卷 4172-4189页

作者： Sarvi, M. Parpaei, M. Bagheri, H. Kojoori, M. R. Alkaei Imam Khomeini Int Univ Tech & Engn Fac Qazvin Iran

This paper presents a novel maximum power point control of stand-alone proton exchange membrane fuel cell (PEMFC) system using a novel optimization strategy based on the eagle strategy (ES) coupled with cuckoo optimization algorithm (COA) and particle swarm optimization (PSO) in order to track the maximum power point of PEMFC. The proposed method presents a new combination of optimization algorithms. According to this strategy, the advantages of optimization algorithms beside each other lead to reach better results in term of the convergence speed and the accuracy of the optimum operation of the system. Furthermore, the standard deviation of data produced by the proposed method is very lower than other conventional optimization algorithms;PSO and COA algorithms, so it leads to decrease the output power oscillation. To validate these advantages, the proposed tracker applied to the PEMFC system in MATLAB/SIMULINK environment. The simulation results confirm the effectiveness and accuracy of the proposed approach.

关键词： Maximum power point tracking Intelligent optimization algorithm PEMFC Particle swarm optimization cuckoo optimization algorithm

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