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    A new approach to optimization of cogeneration systems using genetic algorithm

    , Article International Journal of Energy and Environmental Engineering ; Volume 1, Issue 1 , 2010 , Pages 37-48 ; 20089163 (ISSN) Zomorodian, R ; Rezasoltani, M ; Ghofrani, M. B ; Sharif University of Technology
    2010
    Abstract
    Application of Cogeneration systems based gas turbine for heat and power production is increasing. Because of finite natural energy resources and increasing energy demand the cost effective design of energy systems is essential. CGAM problem as a cogeneration system is considered here for analyzing. Two new approaches are considered, first in thermodynamic model of gas turbine and cogeneration system considering blade cooling of gas turbine and second using genetic algorithm for optimization. The problem has been optimized from thermodynamic and thermoeconomic view point. Results show that Turbine Inlet Temperature (TIT) in thermodynamic optimum condition is higher than thermoeconomic one,... 

    Artificial intelligence techniques for modeling and optimization of the HDS process over a new graphene based catalyst

    , Article Phosphorus, Sulfur and Silicon and the Related Elements ; Volume 191, Issue 9 , 2016 , Pages 1256-1261 ; 10426507 (ISSN) Hajjar, Z ; Kazemeini, M ; Rashidi, A ; Tayyebi, S ; Sharif University of Technology
    Taylor and Francis Ltd  2016
    Abstract
    A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H2/feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg–Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a... 

    Forecasting smoothed non-stationary time series using genetic algorithms

    , Article International Journal of Modern Physics C ; Volume 18, Issue 6 , 2007 , Pages 1071-1086 ; 01291831 (ISSN) Norouzzadeh, P ; Rahmani, B ; Norouzzadeh, M. S ; Sharif University of Technology
    2007
    Abstract
    We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time... 

    A hybrid method of neural networks and genetic algorithm in econometric modeling and analysis

    , Article Journal of Applied Sciences ; Volume 8, Issue 16 , 2008 , Pages 2825-2833 ; 18125654 (ISSN) Hasheminia, H ; Akhavan Niaki,S. T ; Sharif University of Technology
    2008
    Abstract
    In this study a hybrid method of neural networks-genetic algorithms is proposed and applied in an economical case study. The results of this study show that the proposed hybrid algorithm is a more efficient modeling approach compared to either a single neural network method or a single genetic algorithm approach. Since modeling based on the observed data is also employed in other fields of science, the application of the proposed method is not restricted only to economics. © 2008 Asian Network for Scientific Information  

    Which method is better for the kinetic modeling: decimal encoded or binary genetic algorithm?

    , Article Chemical Engineering Journal ; Volume 130, Issue 1 , 2007 , Pages 29-37 ; 13858947 (ISSN) Boozarjomehry, R. B ; Masoori, M ; Sharif University of Technology
    2007
    Abstract
    Kinetic modeling is an important issue, whose objective is the accurate determination of the rates of various reactions taking place in a reacting system. This issue is a pivotal element for the process design and development particularly for novel processes which are based on reactions taking place between various types of species. In this paper, the Genetic Algorithms have been used to develop a systematic computational framework for kinetic modeling of various reacting systems. This framework can be used to find the optimum values of various parameters that exist in the kinetic model of a reacting system. The Fischer-Tropsch (FT) reactions have been used as the kinetic modeling bench... 

    Comparing Performance of M.V, E.G.P and M.V.S Based on Genetic Algorithm in Iranian Capital Market

    , M.Sc. Thesis Sharif University of Technology Sanati, Ali (Author) ; Bahramgiri, Mohsen (Supervisor)
    Abstract
    The portfolio selection problem is always one of the most important problems of finance and investments due to its great implication and vital role in financial institutions. Many of researches in this area are based on the mean-variance model, originally proposed by Markoitz. In the last two decades, however, researchers and investors have attracted to some new models that import some new factors other than mean and variance in the portfolio decision problem, such as different risk measures, etc. In this research we compare performances of mean-variance, Elton-Gruber-Padberg (EGP) and mean-variance-skewness based on genetic algorithm in Tehran Stock Exchange. Moreover, in order to find the... 

    A Quantitative Structure-Activity Relationship Study on Multiple Sclerosis (MS) Drugs

    , M.Sc. Thesis Sharif University of Technology Torkashvand, Rezvan (Author) ; Jalali-Heravi, Mehdi (Supervisor)
    Abstract
    In the present work we report a quantitative structure-activity relationship (QSAR) study on S1P1 receptor’s agonists that have therapeutic potential for autoimmune disorders such as Multiple Sclerosis (MS). Such studies play an important role in drug design and lead optimization by developing a mathematical relationship between the chemical structures of compounds and their biological activities.
    We used both linear and nonlinear techniques such as MLR and ANN respectively to model these compounds together with techniques such as Stepwise-MLR, GA-MLR and GA-ANN in the variable selection step as it is an important step in every QSAR study. Since topological descriptors are well... 

    Hydrodynamic optimization of marine propeller using gradient and non-gradientbased algorithms

    , Article Acta Polytechnica Hungarica ; Volume 10, Issue 3 , 2013 , Pages 221-237 ; 17858860 (ISSN) Taheri, R ; Mazaheri, K ; Sharif University of Technology
    2013
    Abstract
    Here a propeller design method based on a vortex lattice algorithm is developed, and two gradient-based and non-gradient-based optimization algorithms are implemented to optimize the shape and efficiency of two propellers. For the analysis of the hydrodynamic performance parameters, a vortex lattice method was used by implementing a computer code. In the first problem, one of the Sequential Unconstraint Minimization Techniques (SUMT) is employed to minimize the torque coefficient as an objective function, while keeping the thrust coefficient constant as a constraint. Also, chord distribution is considered as a design variable, namely 11 design variables. In the second problem, a modified... 

    An optimized neural network model of desalination by vacuum membrane distillation using genetic algorithm

    , Article CHISA 2012 - 20th International Congress of Chemical and Process Engineering and PRES 2012 - 15th Conference PRES ; 2012 Tavakolmoghadam, M ; Safavi, M ; Sharif University of Technology
    2012
    Abstract
    An experimental based ANN model is constructed to describe the performance of vacuum membrane distillation process for desalination in different operating conditions. The vacuum pressure, feed inlet temperature, concentration of the feed salt aqueous solution, and feed flow rate are the input variables of this process, while response is the permeate flux. The neural network approach is capable for modeling this membrane distillation configuration. The application of Genetic Algorithm to optimize the ANN model parameters was also examined. This is an abstract of a paper presented at the CHISA 2012 - 20th International Congress of Chemical and Process Engineering and PRES 2012 - 15th... 

    An optimized neural network model of desalination by vacuum membrane distillation using genetic algorithm

    , Article Procedia Engineering ; Volume 42 , 2012 , Pages 106-112 ; 18777058 (ISSN) Tavakolmoghadam, M ; Safavi, M ; Sharif University of Technology
    Abstract
    An experimental based ANN model is constructed to describe the performance of vacuum membrane distillation process for desalination in different operating conditions. The vacuum pressure, the feed inlet temperature, the concentration of the feed salt aqueous solution and the feed flow rate are the input variables of this process, whereas the response is the permeate flux. The neural network approach was found to be capable for modeling this membrane distillation configuration. The application of Genetic Algorithm (GA) to optimize the ANN model parameters was also investigated  

    Genetic algorithm for solving fuzzy shortest path problem in a network with mixed fuzzy arc lengths

    , Article AIP Conference Proceedings, 2 December 2010 through 4 December 2010, Sarawak ; Volume 1337 , 2011 , Pages 265-270 ; 0094243X (ISSN) ; 9780735408937 (ISBN) Mahdavi, I ; Tajdin, A ; Hassanzadeh, R ; Mahdavi-Amiri, N ; Shafieian, H ; Sharif University of Technology
    2011
    Abstract
    We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model  

    Fault diagnosis in a yeast fermentation bioreactor by genetic fuzzy system

    , Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 29, Issue 3 , 2010 , Pages 61-72 ; 10219986 (ISSN) Tayyebi, S ; Shahrokhi, M ; Bozorgmehry Boozarjomehry, R ; Sharif University of Technology
    Abstract
    In this paper, the fuzzy system has been used for fault detection and diagnosis of a yeast fermentation bioreactor based on measurements corrupted by noise. In one case, parameters of membership functions are selected in a conventional manner. In another case, using certainty factors between normal and faulty conditions the optimal values of these parameters have been obtained through the genetic algorithm. These two cases are compared based on their performances in fault diagnosis of a yeast fermentation bioreactor for three different conditions. The simulation results indicate that the fuzzy-genetic system is superior in multiple fault detection for the conditions where the minimum and... 

    A model based approach on multi-agent system and genetic algorithm to improve the process management in service oriented architecture

    , Article Journal of Telecommunication, Electronic and Computer Engineering ; Volume 8, Issue 5 , 2016 , Pages 33-40 ; 21801843 (ISSN) Nahvi, B ; Habibi, J ; Sharif University of Technology
    UniversityTeknikal Malaysia Melaka  2016
    Abstract
    Service oriented architecture is based on the provision of services. To enhance the performance of the systems by providing a better combination of services, it is necessary to extract more information compared to the one in the service registry. In this regard, the accomplished works have been focusing on the basic concepts of service-oriented architecture. The service composition is based on the information in service registry, provided by the service provider. Further, centralized combination with insufficient information does not meet the system performance requirements. This solution helps to facilitate resource distribution and reduces tasks of the central unit. In this paper, efforts... 

    Economic design of phase 2 simple linear profiles with variable sample size

    , Article International Journal of Productivity and Quality Management ; Volume 18, Issue 4 , 2016 , Pages 518-536 ; 17466474 (ISSN) Ershadi, M. J ; Noorossana, R ; Akhavan Niaki, S. T ; Sharif University of Technology
    Inderscience Enterprises Ltd  2016
    Abstract
    Employing profiles using variable sample size (VSS) may improve its effectiveness and hence is investigated in this paper. Besides, the implementation cost of a profile is an important factor in determining the main parameters. In this paper, the Lorenzen-Vance function is first extended to model implementation cost of VSS linear profiles. Then, the resulted economic model is solved using a genetic algorithm (GA). The average run length criterion when process is in control, ARL0 and the average run length measure when process goes out of control, ARL1 , is used to evaluate statistical properties of the designed profile. Moreover, a sensitivity analysis on the main parameters of the... 

    A two-part self-adaptive technique in genetic algorithms for project scheduling problems

    , Article Journal of Modern Project Management ; Volume 4, Issue 2 , 2016 , Pages 64-73 ; 23173963 (ISSN) Shahsavar, A ; Akhavan Niaki, S. T ; Najafi, A. A ; Sharif University of Technology
    Editora Mundos Sociais  2016
    Abstract
    The present paper introduces a novel two-part self-adaptive technique in designing the genetic algorithm for project scheduling problems. One part of the algorithm includes a self-adaptive mechanism for genetic operators like crossover and mutation. The second part contains another self-adaptive mechanism for genetic parameters such as crossover probability. The parts come in turn repeatedly within a loop feeding each other with the information regarding the performance of operators or parameters. The capability of the method is tested and confirmed in comparison to metaheuristic and exact algorithms based on well-known benchmarks  

    A bi-objective multi-facility location-allocation problem with probabilistic customer locations and arrivals: two meta-heuristics using discrete approximation

    , Article Journal of Uncertain Systems ; Volume 12, Issue 2 , 2018 , Pages 123-140 ; 17528909 (ISSN) Mohammadivojdan, R ; Akhavan Niaki, S. T ; Dadashi, M ; Sharif University of Technology
    World Academic Union  2018
    Abstract
    In this work, a bi-objective multi-facility location-allocation problem is investigated, in which the locations of the customers and their arrivals are stochastic. We first formulate the problem as a continuous location-allocation model with no constraints on the capacity of the facilities. Then, we develop an approximated discrete model in which the facilities with limited capacities can be located on a set of candidate points. The proposed model has two objective functions that are evaluated using discrete event system simulation. The first objective is to minimize the expected total time the customers spend in the system until their services begin. The time that each customer spends in... 

    Cost-based differential pricing for a make-to-order production system in a competitive segmented market

    , Article Journal of Revenue and Pricing Management ; Volume 19, Issue 4 , 2020 , Pages 266-275 Teimoury, E ; Modarres, M ; Neishaboori, M ; Sharif University of Technology
    Palgrave Macmillan  2020
    Abstract
    Optimal queuing system design includes deciding about some variables such as location, capacity, price, and delivery time. In order to develop an optimized system, it is essential to solve the optimization problem for all the decision variables simultaneously. In this paper, a make-to-order system is considered. In this system, some facilities are developed in different points. According to the make-to-order system definition, these facilities keep no inventory from final product. Orders are received from customers and the requested products are assembled. Due to existence of some differences among customers, they are divided into two distinct categories including express and regular... 

    Finding the sensors location and the number of sensors in sensor networks with a genetic algorithm

    , Article Proceedings of the 2008 16th International Conference on Networks, ICON 2008, 12 December 2008 through 14 December 2008, New Delhi ; 2008 ; 9781424438051 (ISBN) Robatmili, M ; Mohammadi, M ; Movaghar, A ; Dehghan, M ; Sharif University of Technology
    2008
    Abstract
    Sensor networks have recently emerged as a premier research topic. Sensor networks pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. While designing the sensor networks according to performed computation, the limited number of sensors to cover an area will be considered, so the proper placing of this limited number of sensors will cause costs to reduce regarding to coverage and development of the network in the next stage. In this paper we will present a genetic algorithm to solve the designing issue of the sensor network. The most important... 

    Application of the genetic algorithm to calculate the interaction parameters for multiphase and multicomponent systems

    , Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 26, Issue 3 , 2007 , Pages 89-102 ; 10219986 (ISSN) Rashtchian, D ; Ovaysi, S ; Taghikhani, V ; Ghotbi, C ; Sharif University of Technology
    2007
    Abstract
    A method based on the Genetic Algorithm (GA) was developed to study the phase behavior of multicomponent and multiphase systems. Upon application of the GA to the thermodynamic models which are commonly used to study the VLE, VLLE and LLE phase equilibria, the physically meaningful values for the Binary Interaction Parameters (BIP) of the models were obtained. Using the method proposed in this work the activity coefficients for components at infinite dilution, obtained from the local composition based models, can be accurately predicted comparing to the experimental data available in the literature. In this work, a Global Optimization Procedure (GOP) based on the GA was developed to obtain... 

    Kinetic mechanism reduction using genetic algorithms, case study on H 2/O2 reaction

    , Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 26, Issue 3 , 2007 , Pages 1-9 ; 10219986 (ISSN) Abedini, H ; Pishvaie, M. R ; Bozorgmehry, R ; Sharif University of Technology
    2007
    Abstract
    For large and complex reacting systems, computational efficiency becomes a critical issue in process simulation, optimization and model-based control. Mechanism simplification is often a necessity to improve computational speed. We present a novel approach to simplification of reaction networks that formulates the model reduction problem as an optimization problem and solves it using genetic algorithm (GA).The aim of simplification kinetics modeling is to derive the simplest reaction system, which retains the essential features of the full system. Numerical results for H2/O2 combustion reaction mechanism illustrate the potential and proficiency of this approach