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A short discussion about "Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm"

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APPLIED THERMAL ENGINEERING 2015年 76卷 535-537页

作者： Fister, Iztok, Jr. Fister, Iztok Yang, Xin-She Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Middlesex Univ Sch Sci & Technol London NW4 4BT England

This short discussion concerns some inaccuracy in the recently published paper "M. Asadi et al. Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm. Applied thermal engineering, 2014". There was a mix-up between the cuckoo search (CS) with the cuckoo optimization algorithm (COA), which may cause further confusion if uncorrected. In this discussion, we intend to clarify the differences between both algorithms so as to provide a more accurate description of both algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： cuckoo search cuckoo optimization algorithm Swarm intelligence

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A hybrid method for forecasting river-suspended sediments in Iran

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INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT 2017年第4期15卷 453-460页

作者： Targhi, Alireza Tavakoli Abbaszadeh, Sadegh Arabasadi, Zeinab Shahid Beheshti Univ CS Grp Math Dept Tehran Iran Payame Noor Univ Dept Math Tehran Iran Univ Bojnord Dept Comp Sci Bojnord Iran

Estimation of sediment mass carried by rivers is an important issue in Hydrological Sciences. The main purpose of this research was to find an appropriate method to compute sediment discharge. Some machine-learning approaches have been used to forecast river-suspended sediments, correctly. One of the most effective and traditional approaches for forecasting events is to use artificial neural networks (ANNs). So, we are going to improve the performance of ANNs in estimation of suspended sediments, upon a data of Baba Aman basin in Iran. We first apply a typical neural network and obtain the root-mean-square-error of 1.138 and the correlation coefficient of 0.90363. Then, to improve the prediction ability of ANNs, we hybridize this method with cuckoo optimization algorithm (COA). Combination of ANNs with COA causes reduction in root-meansquare-error to 0.0960, increasing in correlation coefficient to 0.99975 and also proposing a better model.

关键词： Suspended sediments artificial neural network cuckoo optimization algorithm discharge

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Optimized Fuzzy-cuckoo Controller for Active Power Control of Battery Energy Storage System, Photovoltaic, Fuel Cell and Wind Turbine in an Isolated Micro-Grid

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BATTERIES-BASEL 2017年第3期3卷 23-23页

作者： Einan, Mohsen Torkaman, Hossein Pourgholi, Mahdi Shahid Beheshti Univ Fac Elect Engn Tehran *** Iran

This paper presents a new control strategy for isolated micro-grids including wind turbines (WT), fuel cells (FC), photo-voltaic (PV) and battery energy storage systems (BESS). FC have been used in parallel with BESSs in order to increase their lifetime and efficiency. The changes in some parameters such as wind speed, sunlight, and consumption, lead to improper performance of droop. To overcome this challenge, a new intelligent method using a combination of fuzzy controller and cuckoo optimization algorithm (COA) techniques for active power controllers in isolated networks is proposed. In this paper, COA is compared with genetic algorithm (GA) and particles swarm optimization algorithm (PSO). In order to show efficiency of the proposed controller, this optimal controller has been compared with droop, optimized droop, and conventional fuzzy methods, the dynamic analysis of the island is implemented to assess the behavior of isolated generations accurately and simulation results are reported.

关键词： battery energy storage system fuel cell photo-voltaic active power control cuckoo optimization algorithm optimal fuzzy logic controller genetic algorithm isolated micro-grid

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Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic algorithms

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INFORMATION 2017年第1期8卷 31页

作者： Chen, Jeng-Fung Lo, Shih-Kuei Do, Quang Hung Feng Chia Univ Dept Ind Engn & Syst Management Taichung 40724 Taiwan Univ Transport Technol Fac Informat Technol Hanoi 100000 Vietnam

Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search algorithm (GSA) and cuckoo optimization algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.

关键词： cuckoo optimization algorithm Gravitational Search algorithm neural networks forecasting monthly electricity demand

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Optimal Location of Unified Power Quality Conditioner in Distribution System for Power Quality Improvement

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 83卷 309-324页

作者： Sarker, Jayanti Goswami, S. K. Techno India Dept Elect Engn Kolkata 700091 W Bengal India Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

The increased use of nonlinear loads in distribution system is increasing the distortion in the voltage and current waveforms. Moreover, the distribution systems are inherently unbalanced. This paper presents cuckoo optimization algorithm (COA) based Unified Power Quality Conditioner (UPQC) allocation in three phase unbalanced distribution network. The performance of UPQC is studied in terms of minimization of load disturbance during fault condition in the test systems, % reduction of total harmonic distortion and individual harmonics, minimization of real power loss, decrease in voltage unbalance and increase in cost savings during normal operating condition. The performance of COA is compared with other soft computing techniques to get the better results, i.e., better voltage profile, the optimal location and optimal number of UPQCs. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Unified Power Quality Conditioner (UPQC) cuckoo optimization algorithm Optimal placement problem Total harmonic distortion minimization Real power loss minimization Load disturbance minimization during fault

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Estimation of the CO₂-oil minimum miscibility pressure for enhanced oil recovery

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第22期34卷 1847-1854页

作者： Choubineh, Abouzar Mousavi, Seyede Robab Ayouri, Masih Vafaee Ahmadinia, Masoud Choubineh, Dariush Baghban, Alireza Petr Univ Technol Petr Dept Ahvaz Iran Shiraz Univ Petr Dept Shiraz Iran Petr Univ Technol Automat & Instrumentat Dept Ahvaz Iran Polytech Univ Turin Petr Dept Turin Italy Islamic Azad Univ Petr Dept Gachsaran Iran Islamic Azad Univ Marvdasht Branch Young Researcher & Elite Club Marvdasht Iran

The authors' aim was to present new models based on artificial neural network (ANN) and two optimization algorithms including cuckoo optimization algorithm (COA) and teaching learning based optimization (TLBO) to predict the pure and impure CO2 MMP. Thirty-four and 11 training and testing data sets were used to develop these models with following inputs: reservoir temperature, the mole percent of volatile oil components (C-1 and N-2), mole percent of intermediate oil components (C-2-C-4, CO2, and H2S), molecular weight of C5+ fraction in oil phase (MWC5+) and mole percentage of CO2, N-2, C-1, C-4, and H2S in the injected gas. Statistical comparisons show that although two models yield acceptable results, the ANN-TLBO model has better performance with the lower mean absolute percentage error (2.6%) and standard deviation (3.37%) and the higher coefficient of determination (0.993). Moreover, among the available correlations, the Cronquist's (1978;corrected by Sebastian etal., 1985) correlations have better performance. Finally, the sensitivity analysis on the ANN-TLBO showed that MWC5+ and reservoir temperature are the most influential parameters in determining the CO2 MMP, respectively.

关键词： Artificial neural network cuckoo optimization algorithm minimum miscibility pressure sensitivity analysis teaching learning-based optimization

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optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

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ENGINEERING optimization 2016年第4期48卷 543-561页

作者： Hosseinzadeh, Ali Zare Amiri, Gholamreza Ghodrati Koo, Ki-Young Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran Iran Univ Exeter Coll Engn Math & Phys Sci Vibrat Engn Sect Exeter Devon England

This article presents an effective method for structural damage identification. The damage diagnosis problem is introduced as an optimization problem which is based on computing static displacements by the flexibility matrix. By utilizing this matrix, the complexity of the static displacement measurements in real cases can be overcome. The optimization problem is solved by a fast evolutionary optimization strategy, named the cuckoo optimization algorithm. The performance of the presented method was demonstrated by studying the benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring, and a numerical example of a frame. Moreover, the robustness of the presented approach was investigated in the presence of some prevalent modelling errors, and also when noisy and incomplete modal data are available. Finally, the efficiency of the proposed method was verified by an experimental study of a five-storey shear building structure. All the obtained results show the good performance of the presented method.

关键词： damage detection modal data flexibility matrix static displacement cuckoo optimization algorithm

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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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optimization of the reflux ratio of benzene-toluene stage distillation columns by the cuckoo algorithm

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Petroleum Science 2014年第3期11卷 446-453页

作者： Bahador Abolpour Ali Mohebbi Department of Chemical Engineering Faculty of Engineering Shahid Bahonar University of Kerman

In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, there are relatively few degrees of freedom that can be manipulated in order to minimize the total annualized cost. The reflux ratio can influence the steady-state operating point and therefore influence the total annualized cost. The trade-offs between reflux ratios and total annualized cost were discussed. The cuckoo optimization algorithm was applied to obtain a correlation for the optimum value of the reflux ratio as a power function of the economic parameters of energy price and capital cost. The results show that, at low energy price or high capital cost, the optimum reflux factor is high.

关键词： Benzene-toluene mixture distillation column cuckoo optimization algorithm optimized reflux ratio total annualized cost

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Order Reduction of Linear Continuous Time Interval System using Mixed Evolutionary Technique

Order Reduction of Linear Continuous Time Interval System us...

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International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

作者： Tanwar, Narendra Singh Bhatt, Rajesh Parmar, Girish Rajasthan Tech Univ Dept Elect Engn Kota India

ISBN: (纸本)9781467395458

Higher order systems are difficult to control and complex in nature hence, it is desirable to reduce these systems in the low order system. A system with constant coefficient but uncertain within finite range is known as interval system. This paper aims to reduce linear continuous time interval system using evolutionary technique. cuckoo optimization algorithm technique has attracted vast attention among various evolutionary techniques. The Routh array is constructed to obtain denominator of higher order system using the generalized routh table. The numerator of reduced order system is obtained using cuckoo optimization algorithm by minimizing integral square error between original and reduced order system. Reduced order system retains the stability and steady state value of the higher order system. The numerical examples are illustrated and results are compared with the other well known methods.

关键词： large scale interval system order reduction interval arithmatics cuckoo optimization algorithm routh approximation integral square error

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