2014
3
1
1
0
Velocity Control of Electro Hydraulic Servo System by Tracking Method
2
2
This paper proposes an efficient Tracking method for velocity control of an electrohydraulic servo system (EHSS) in the presence of flow nonlinearities and internal friction. The tracking method controller is a kind of feedback error learning structure. In the proposed method, the Feedback Error Learning (FEL) algorithm is used to control the velocity. There is no need to compute the system jacobian in FEL method which in turn makes its using more suitable for practical scenarios. This procedure illustrates that EHSS control can be successfully. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.
1

1
6


Mohammad Reza
Asadi Asad Abad
Department of Mechanical Engineering BuinZahra Branch, Islamic Azad University Buinzahra, Iran.
Department of Mechanical Engineering BuinZahra
Iran
azare@buiniau.ac.ir


Amir Reza
Zare Bidaki
Young Researchers and Elite Club, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran.
Young Researchers and Elite Club, Buinzahra
Iran


Mohsen
Jahanshahi
Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Young Researchers and Elite club, Central
Iran
mjahanshahi@iauctb.ac.ir
Electro Hydraulic Servo System (EHSS)
Feedback Error Learning (FEL)
Laguerre Controller
[H. E. Merritt, “Hydraulic Control System”, New York: John Wiley & Sons, Inc., 1967. ##J. Watton, “Fluid Power System”, New York: Prentice Hall, 1989. ##M. Jovanovic “Nonlinear Control of an Electro Hydraulic Velocity Servo System”, ACC 02, Anchorage, Alaska, USA, 2002. ##S. A. Mohseni M. Aliyari.sh and M. Teshnehla,”EHSS Velocity Control by Fuzzy Neural Networks”, IEEE, North American Fuzzy Information Processing Society, pp.1318, 2006. ##S. A. Mohseni, M, Aliyari Shooredeli, M, Teshnehlab, “Decoupled SlidingMode with Fuzzy Neural Network Controller for EHSS Velocity Control”, Intelligent and Advanced Systems, ICIAS, Malaysia, 2007. ##H. Azimian, R. Adlgostar and M.Teshnelab, “Velocity Control of an Electro Hydraulic Servomotor by Neural Networks”, International Conference PhysCon, Saint Petersburg, Russia, pp.2426, 2005. ##Chan L. Asokanthan. “CMAC Based Controller for Hydro Mechanical Systems”, American Control Conference ACC`01, Arlington, VA, SA, 2001. ##H. Miyamoto, M. Kawato, T. Setoyama and R. Suzuki, “Feedback Error Learning Neural Network for Trajectory Control of a Robotic Manipulator Neural Networks”, Vol.1, pp.251265, 1988. ##M. Kawato, “Computational Schemes and Neural Network Models for Formation and Control of Multi Joint Arm Trajectory” in W. T. Miller, R. S. Sutton, and P. J. Werbos, Neural Networks for Control, The MIT Press, 1990. ##Tomas Oliveira e Silva “Laguerre Filters – An Introduction” Revista do DETUA, Vol.1, No.3, pp.237248, 1995. ##M. Asadi, F. Razzazi, “Adaptive Determination of the Free Parameters of Generalized Orthonormal IIR Adaptive Filters Using Genetic Algorithm”, Proc. IEEE Computer Control and Communication, 2009. ##A. R. Zare Bidaki,. M Malboubi, M Jahanshahi, R. Pirmoradi and M R Asadi , “Velocity Control of an Electro Hydraulic Servo System Using an Efficient LaguerreBased Controller”, Australian Journal of Basic and Applied Sciences, Vol.5, No.12, pp.19341942, 2011. ##M. Tavan, M. Aliyari.sh and A. R. Zare Bidaki, “Stability of Feedback Error Learning for Linear Systems”, Milano, Italy, IFAC, 2011. ##M. H Shafiabadi, M Jahanshahi, and A. R. Zare Bidaki, “Feedback Error Learning Using LaguerreBased Controller to Control the Velocity of an Electro Hydraulic Servo System”, Australian Journal of Basic and Applied Sciences, Vol.6, No.10, pp.222230, 2012.##]
Novel Approach for Optimal Sizing of Standalone Hybrid Photovoltaic/Wind Systems
2
2
Nowadays using of new energies in the form of dispersed resources in the worlds is wide spreading. In this article we will
design a dispersed production source in the form of a solar/wind hybrid power plant in order to supply the energy of a residential
unit according to a sample load pattern. The aim of aforementioned design is to reduce its costs in a period of 20 years. In order
to optimize system costs we will use a new algorithm which is based on collective intelligence namely gravitational search
algorithm and also we will use particle swarm optimization algorithm. At the end of the article we will present anemometry
and radiation data which is collected from an area in Ardebil and Mashhad, note that these data is optimized based on our
suggested algorithm. Finally we can conclude that with an appropriate design of dispersed production resources we will be
able to effectively reduce costs and make renewable energy usage more economically.
1

7
15


Zakieh
Tolooi
Science and research, Islamic Azad University, Kerman, Iran
Science and research, Islamic Azad University,
Iran
zakiye_tolooee@yahoo.com,


Hadi
Zayandehroodi
Science and research, Islamic Azad University, Kerman, Iran
Science and research, Islamic Azad University,
Iran
h.zayandehroodi@yahoo.com


Alimorad
Khajehzadeh
Science and research, Islamic Azad University, Kerman, Iran
Science and research, Islamic Azad University,
Iran
ali.khajezadeh@yahoo.com
Renewable Energies
Energy Hybrid Systems
GSA
PSO
Sizing
[[1] Koutroulis , D. Kolokotsa, A. Potirakis, and K. Kalaitzakis, ##“Methodology for Optimal Sizing of StandAlone ##Photovoltaic/WindGenerator Systems Using Genetic ##Algorithms”, Solar Energy, Vol.80, pp.10721088, 2006. ##[2] A. K. Kaviani, H. R. Baghaie, and G. Riahi , “Optimal Sizing ##of a Hybrid PV/WG Using PSO”, In Proceedings of 22th ##Power System Conference (PSC 2007), Tehran, Iran, 2007. ##[3] F. Jahanbani A., G. H. Riahy, and M. Abedi, “Optimal Sizing ##of a StandAlone Hybrid Wind/PV/Battery System ##Considering Reliability Indices Accompanied by Error ##Propagation Assessment”, International Review of Electrical ##Engineering (I.R.E.E), Vol.5, No.2, pp.748757, MarchApril ##[4] M. Dali, J. Belhadj, and X. Roboam, “Design of a Stand ##Alone Hybrid PhotovoltaicWind Generating System with ##Battery Storage”, ICEEDT Conference, 2008. ##[5] S. Diaf, M. Belhamel, M. Haddadi, and A. Louche, “A ##Methodology for Optimal Sizing of Autonomous Hybrid ##PV/Wind System, Energy Policy, Vol.35, No.11, 2007. ##[6] W. Zhou, C. Lou, Z. Li, L. Lu and H. Yang, “Current Status ##of Research on Optimum Sizing of StandAlone Hybrid ##Solar–Wind Power Generation Systems”, Applied Energy, ##Vol.87, pp.380389, 2010. ##[7] Z. Tolooee, H. ZayandehRoodi, A. KhajeZadeh, “Review of ##Optimal Sizing Problem of Hybrid Renewable Energy forStand Alone Systems”, Third Conference of Clean Energy, Kerman, Iran, 2013. ##H. Yang, W. Zhou, L. Lu and Z. Fang, “Optimal Sizing Method for StandAlone Hybrid Solar–Wind System with LPSP Technology by Using Genetic Algorithm”, Solar Energy, Vol.82, pp.354367, 2008. ##S. M. Hakimi, and S. M. MoghaddasTafreshi, “Optimal Sizing of a StandAlone Hybrid Power System Via Particle Swarm Optimization for Kahnouj Area in SouthEast of Iran”, Renewable Energy, Vol.34, pp.18551862, 2009. ##E. Rashedi , H. Nezamabadipour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm”, Information Sciences, Vol.179, No.13, pp.22322248, June 2009.##]
Intelligence Method for PID Controller Design in AVR System
2
2
Designing of a PID controller is a very common method for industrial process control and due to its very simple and efficient function; it is used in a wide variety of industrial applications. PID controller to reduce the steady state error and dynamic response of the system is used. PID controller design is an inevitable problem in setting the coefficients need to try a lot of trial and error, therefore the optimization of parameters in this controller is attention of many researcher and there are many methods to find optimal parameters of PID controller. Fast and exactly adjustment of the parameters optimized controller is to create high quality answers. In this paper, an optimized tuning method for PID controller is presented. In this method the PSO algorithm is used to design the parameters of an AVR (Automatic Voltage Regulation) system using various fitness functions. Easy implementation, stable convergence characteristic and high computational efficiency are among advantages of presented method.
1

17
22


Seyyed Amir
Hashemi Zadeh1
Department of electrical engineering, islamic azad university branch Rafsanjan ,Iran
Department of electrical engineering, islamic
Iran
sa_hashemizadeh@yahoo.com


Mostafa
Zamani Mohi Abadi
Faculty Member, Research Department of High Temperature Fuel Cell, ValieAsr University of Rafsanjan, Iran
Faculty Member, Research Department of High
Iran
m.zamani@vru.ac.ir
PID controller
PSO algorithm
Optimization
AVR system
[[1] R. A. Krohling and J. P. Rey, “Design of Optimal ##Disturbance Rejection PID Controllers Using Genetic ##Algorithm”, IEEE Trans. Evolutionary Computation, Vol.5, ##Iss.1, pp.7882, Feb 2001. ##[2] J. G. Ziegler and MB. Nichols., “Optimum Settlings for ##Automatic Controllers”, Trans. On ASME., Vol.64 , ##pp.759768, 1942. ##[3] C.C. Hang, K.J. Astrom, W.K. Ho., “Refinements of The ##Ziegler Nichols Tuning Formula”, IEE proceedings ##Control Theory and Applications, Vol.138, No.2, pp.111 ##118, March 1991. ##[4] A.Visioli, “Tuning of PID Controllers With Fuzzy Logic”, ##IEE proc. Control Theory App., Vol.148, pp.18, January ##[5] Paul Acarnley, “Tuning PI Speed Controllers for Electric ##Drives Using Simulated Annealing”, IEEE International ##Symposium on Industrial Electronics, pp.11311135, 2002. ##[6] E. Salim Ali and S. M. AbdElazim., “ Optimal PID Tuning ##for Load Frequency Control Using Bacteria Foraging ##Optimization Algorithm”, Proceedings of the 14th ##International Middle East Power Systems Conference ##(MEPCON’10), Cairo University, Egypt, PaperID191, ##Dec.1921, 2010. ##[7] A. AhmadiJavid., “Anarchic Society Optimization: A ##Human Inspired Method, Applied Soft Computing”, In ##Proc. of IEEE Congress on Evolutionary Computation, New ##Orleans, LA, pp.25862592, 2011. ##[8] H. Gozde, M.C. Taplamacioglu., “Application Of Artificial ##Bee Colony Algorithm in an Automatic Voltage Regulator ##(AVR) System”, International Journal on Technical and ##Physical Problems of Engineering, Vol.1, No.3, pp.8892, ##[9] V. Mukherjee, S.P. Ghoshal., “Intelligent Particle Swarm ##Optimized Fuzzy PID Controller for AVR System”, Electric ##Power Systems Research, Vol.77, pp.16891698, 2007. ##[10] A.K. Kaviani, H.R. Baghaei, Gh.H. Riahi, “Optimized ##Sizing of a Hybrid Wind Power Plant”, 22th International ##Electrical Conference, Tehran (PSC), 2007. ##[11] M. Abachizadeh, M.R.H. Yazdi, A. Yousefi Koma., ##“Optimal Tuning of PID Controllers Using Artificial Bee ##Colony Algorithm”, Advanced Intelligent Mechatronics ##Conference, pp.379384, 2010. ##[12] Niknam T, Narimani MR, AzizipanahAbarghooee R., “A ##MultiObjective Fuzzy Adaptive PSO Algorithm for ##Location of Automatic Voltage Regulators in Radial ##Distribution Networks”, Int. J. on Control Autom. Syst., ##Vol.10, No.4, pp.772–780, 2012. ##[13] Tang Y,Cui M,Hua C,Li L,Yang Y., “Optimum Design of ##Fractional Order PI kDl Controller for AVR System Using ##Chaotic Ant Swarm”, Expert Syst. Appl., 2012. ##[14] Adel A. A. ElGammal1 Adel A. ElSamahy., “A Modified ##Design of PID Controller For DC Motor Drives Using ##Particle Swarm Optimization PSO”, 1Energy Research ##Centre, University of Trinidad and Tobago UTT (Trinidad ##and Tobago), Lisbon, Portugal, Mar.1820, 2009.##]
Adaptive Approximate Record Matching
2
2
Typographical data entry errors and incomplete documents, produce imperfect records in real world databases. These errors generate distinct records which belong to the same entity. The aim of Approximate Record Matching is to find multiple records which belong to an entity. In this paper, an algorithm for Approximate Record Matching is proposed that can be adapted automatically with input error patterns. In field matching phase, edit distance method is used. Naturally, it had been customized for Persian language problems such as similarity of Persian characters, usual typographical errors in Persian, etc. In record matching phase, the importance of each field can be determined by specifying a coefficient related to each field. Coefficient of each field must be dynamically changed, because of changes of typographical error patterns. For this reason, Genetic Algorithm (GA) is used for supervised learning of coefficient values. The simulation results show the high abilities of this algorithm compared with other methods (such as Decision Trees).
1

23
27


Ramin
Rahnamoun
Computer Engineering Department, Azad UniversityTehran Central Branch, Tehran, Iran.
Computer Engineering Department, Azad UniversityT
Iran
r.rahnamoun@iauctb.ac.ir
record matching
edit distance
data cleaning
genetic algorithms
[D. E. Goldberg, “Genetic Algorithms in Search Optimization and Machine Learning”, Addison_Wesley, 1989. ##] J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2001. ##M. A. Hernadez, S.J.Stolfo, "Realworld Data is Dirty: Data Cleansing and the Merge/Purge Problem", Journal of Data Mining and Knowledge Discovery, Vol.1, No.2, 1998 ##J.A. Hylton, “Identifying and Merging Related Bibliographical Records”, Master's Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 1996. ##M. Kantardzic,” Data Mining: Concepts, Methods, and Algorithms”, IEEE Press, 2003. ##K. Kukich, "Techniques for Automatically Correcting Words in Text", ACM Computing Survey, Vol.24, No.4, 1992. ##A. E. Monge, “Adaptive Detection of Approximately Duplicate Database Records and Database Integration Approach to Information Discovery”, PHD Thesis, University of California, San Diego, 1997. ##] A. E. Monge, C. P. Elkan, "The Field Matching Problem: Algorithms and Applications" Second International Conference of Knowledge Discovery and Data Mining, AAAI Press, 1996. ##V. S. Verykios, A.K.Elmagarmid, E.H.Houstis, "Automating the Approximate Record Matching Process", Information Science, Vol.126, No 14, 2000. ##V. S. Verykios, G.V.Moustakides, "A Cost Optimal Decision Model for Record Matching", Workshop on Data Quality, 2001.##]
An Improved MPPT Method of Wind Turbine Based on HCS Method by Using Fuzzy Logic System
2
2
In this paper presents a Maximum Power Point Tracking (MPPT) technique based on the Hill Climbing Search (HCS) method and fuzzy logic system for Wind Turbines (WTs) including of Permanent Magnet Synchronous Generator (PMSG) as generator. In the conventional HCS method the step size is constant, therefor both steadystate response and dynamic response of method cannot provide at the same time and in the fixed step size of HCS method. The propose method of this paper is improvement the performance HCS method, in order to reach this goal; the fuzzy logic system has been used. The fuzzy logic system based on operation condition determined the step size instantaneously, such as both steadystate response and dynamic response of method be proper at the same time, therefore, efficiency of the new method that used variable step size strategy, will be guaranteed, the results of simulation in environment MATLAB/Simulink software have been shown to be effectiveness of the proposed method.
1

29
35


Shahram
Javadi
Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
sh.javadi@iauctb.ac.ir


Mohammad Hossein
Hazin
Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran
Electrical Engineering Department, Islamic
Iran
mhhazin@yahoo.com
Wind Turbine
maximum power point tracking
hill climbing search method
fuzzy logic system
[[1] Kot R, Rolak M. Comparison of maximum peak power ##tracking algorithms for a small wind turbine Malinowski. ##Math Comput Simul 2013;91:29e40. ##[2] Abdullah MA, Yatim AHM, Tan CW, Saidur R. A review of ##maximum power point tracking algorithms for wind energy ##systems. Renew Sustain Energy Rev2012; 16:3220e7. ##[3] Abdullah MA, Yatim AHM, Tan CW. A study of maximum ##power point tracking algorithms for wind energy system. In: ##IEEE, first conference on clean energy and technology (CET); ##2011. pp. 321e6. ##[4] Shirazi M, Viki AH, Babayi O. Comparative study of ##maximum power extraction strategies in PMSG wind turbine ##system. In: IEEE electrical power & energy conference; 2009. ##[5] Lei T, Qiang L, Wenzhuo W. A Gaussian RBF network based ##wind speed estimation algorithm for maximum power point ##tracking. Energy Proc 2011;12:27e30. ##[6] Li H, Shi KL, McLaren P. Neural network based sensor less ##maximum wind energy capture with compensated power ##coefficient. IEEE Trans Ind Appl 2005;41(6):1548e56. ##[7] Galdi V, Piccolo A, Siano P. Exploiting maximum energy ##from variable speed wind power generation systems by using ##an adaptive Takagie Sugenoe Kang fuzzy model. Energy ##Convers Manag 2009;50(2):413e21. ##[8] Galdi V, Piccolo A, Siano P. Designing an adaptive fuzzy ##controller for maximum wind energy extraction. IEEE Trans ##Energy 2008;23(2):559e69. ##[9] Ackerman T, editor. Wind power in power systems. John ##Wiley & Sons; 2005. ##R. Datta and V. T. Ranganathan, “A method of tracking the peak power points for variable speed wind energy conversion system,” IEEE Trans. Energy Conversion, vol. 18, no. 1, pp. 163–168, Mar. 2003. ##XingPeng Li, WenLu Fu, QingJun Shi, JianBing Xu, and QuanYuan Jiang “A Fuzzy Logical MPPT Control Strategy for PMSG Wind Generation Systems” journal of electronic science and technology, vol. 11, no. 1, march 2013. ##]
Artificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers
2
2
Protection systems have vital role in network reliability in short circuit mode and proper operating for relays. Current transformer often in transient and saturation under short circuit mode causes maloperation of relays which will have undesirable effects. Therefore, proper and quick identification of Current transformer saturation is so important. In this paper, an Artificial Neural Network (ANN) which is trained by two different swarm based algorithms; Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) have been used to discriminate between Current transformer saturation and fault currents in power transformers. In fact, GSA operates based on gravity law and in opposite of other swarm based algorithms, particles have identity and PSO is based on behaviors of bird flocking. Proposed approach has two general stages. In first step, obtained data from simulation have been processed and applied to an ANN, and then in second step, using training data considered ANN has been trained by GSA & PSO. Finally, a proposed technique has been compared with one of the common training approach which is called Genetic algorithm (GA).
1

37
46


A. R
Moradi
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
Department of Electrical and Computer Engineering,
Iran
eng.alireza.moradi@gmail.com


Y
Alinejad Beromi
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
Department of Electrical and Computer Engineering,
Iran
yalinejad@semnan.ac.ir


K
Kiani
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
Department of Electrical and Computer Engineering,
Iran
kourosh.kiani@ semnan.ac.ir


Z
Moravej
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
Department of Electrical and Computer Engineering,
Iran
zmoravej@semnan.ac.ir
Artificial neural network
Current transformer saturation
Genetic algorithm
Gravitational Search Algorithm
Internal Faults
Particle swarm optimization
Power transformers
[M. S. Sachdev, T. S. Sidhu, and H. S. Gill, “A Busbar Protection Technique and Its Performance during CT Saturation and CT RatioMismatch”, IEEE Transaction on Power Delivery, Vol.15, No.3, pp. 895–901, July 2000. ##C. Fernández, “An ImpedanceBased CT Saturation Detection Algorithm for BusBar Differential Protection”, IEEE Transaction on Power Delivery, Vol.16, No.4, pp.468–472, October 2001. ##Y. Kang, S. Ok, and S. Kang, "A CT Saturation Detection Algorithm”, IEEE Transaction on Power Delivery, Vol.19, No.1, pp.78–85, January 2004. ##N. Villamagna and P. A. Crossley, “A CT Saturation Detection Algorithm Using Symmetrical Components for Current Differential Protection”, IEEE Transaction on Power Delivery, Vol.21, No.1, pp.38–45, January 2006. ##ChiShan Yu, “Detection and Correction of Saturated Current Transformer Measurements Using Decaying DC Components”, IEEE Transaction on Power Delivery, Vol.25, No.3, pp.1340–1347, July 2010. ##Z. Lu, J. S. Smith, and Q. H. Wu, “Morphological Lifting Scheme for Current Transformer Saturation Detection and Compensation”, IEEE Transaction on Circuits and Systems, Vol.55, No.10, pp.3349–3357, November 2008. ##F.B. Ajaei, M. SanayePasand, M. Davarpanah, A. RezaeiZare, R. Iravani, “ Compensation of the CurrentTransformer Saturation Effects for Digital Relays”, IEEE Transaction on Power Delivery, Vol.26, No.4, pp.25312540, October 2011 ##Waldemar Rebizant, and Daniel Bejmert, “Current Transformer Saturation Detection with Genetically Optimized Neural Networks”, IEEE Transaction on Power Delivery, Vol.22, No.2, pp.820–827, April 2007. ##Z. Moravej, D. N. Vishwakarma and S. P. Singh, “Protection and Conditions Monitoring of Power Transformer Using ANN”, Electric Power Components and Systems, Vol.30, Issue 3, pp.217–231, March 2002. ##M. Geethanjali, S. M. R. Slochanal, and R. Bhavani, “PSO trained ANNbased differential protection scheme for power transformers”, Neurocomputing, Vol.71, pp.904–918, April 2007. ##E. Rashedi, H. Nezamabadipour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm”, Information Sciences, Vol.179, pp.2232–2248, March 2009. ##C. Li, and J. Zhou, “Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm”, Energy Conversion and Management, Vol.52, pp.374–381, March 2011.##]
Efficient Data Mining with Evolutionary Algorithms for Cloud Computing Application
2
2
With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of data mining is very important, this paper proposes four faster classification algorithms in comparison with each other. In this paper, A MultiLayer perceptron (MLP) Network is trained with Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Invasive Weed Optimization (IWO) separately. The classifications are done on Wisconsin Breast Cancer (WBC) data base. At the end, to illustrate the speed and accuracy of these classifiers, they are compared with each other and two other types of Genetic algorithm classifiers (GA).
1

47
53


Hamid
Malmir
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
ham.malmir.eng@iauctb.ac.ir


Fardad
Farokhi
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
f_farokhi@iauctb.ac.ir


Reza
SabbaghiNadooshan
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
r_sabbaghi@iauctb.ac.ir
Data Mining
classification
Cloud computing
Imperialist competitive algorithm
Particle swarm optimization
Differential Evolution
Invasive Weed Optimization
[C. Jin, SP. DAI and JL. GUO, “Tutorial of artificial intelligence”, Beijing: Tsinghua University Press, 2007 (In Chinese). ##M. E. GUO and C. Z. XU, “Cloud computing programming model and adaptive resource management”, CCCF, Vol. 7, No. 7, pp. 26–33, July 2011 (In Chinese). ##P. Liu, “Cloud computing”, Beijing: Electronic Industry Press, 2011 (In Chinese). ##K. Keahey, R. Figueiredo, J. Fortes, T. Freeman and M. Tsugawa, “Science Clouds: Early Experiences in Cloud Computing for Scientific Applications”, in Proc. of High Performance Computing and Communications, 2008. ##H. Malmir, F. Farokhi and R. SabbaghiNadooshan, “Optimization of Data Mining with Evolutionary Algorithms for Cloud Computing Application”, International Conference on Computer and Knowledge Engineering (ICCKE), pp. 354–358, 2013. ##K. Chen and WM. Zheng, “Cloud computing: System instances and current research”, Journal of Software, Vol. 20, No. 5, pp. 1337–1348, 2009 (In Chinese). ##P. Wilding, M.A. Morgan, A.E. Grygotis, M.A. Shoffner and E.F. Rosato, “Application of backpropagation neural networks to diagnosis of breast and ovarian cancer”, Cancer Letter, Vol. 77, No. 2–3, pp. 145–153, March 1994. ##P. Tang and Z. Xi, “The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization”, Second Intl. Symp. on Intelligent Information Technology Application, IITA '08, Vol. 2, pp. 13–16, Dec. 2008. ##C. Giannella, K. Liu, T. Olsen and H. Kargupta, “Communication efficient construction of decision trees over heterogeneously distributed data”, in Proc. of the Fourth IEEE Int. Conf. on Data Mining, pp. 67–74, 2004. ##J. Wang, J. Wan, Z. Liu and P. Wang, “Data mining of mass storage based on cloud computing”, in Proc. of Ninth Int. Conf. on Grid and Cooperative Computing, 2010. ##J. Ding and S.Yang,” Classification Rules Mining Model with Genetic Algorithm in Cloud Computing”, Int. Journal of Computer Applications, Vol.48, No.18, pp.24–32, June 2012. ##T. Hu, H. Chen, L. Huang and X. Zhu, “A Survey of Mass Data Mining Based on Cloud computing”, Int. Conf. on AntiCounterfeiting, Security and Identification (ASID), Taipei, pp. 1–4, Aug. 2012. ##E. AtashpazGargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition”, in IEEE Congress on Evolutionary Computation (CEC), pp. 4661–4667, 2007. ##J. Kennedy and R. C. Eberhart, “Particle swarm optimization” , in Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, pp.1942–1948, Nov. 1995. ##P. Angeline, “Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences”, in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E.Eiben, Eds. Berlin, Germany: SpringerVerlag, pp. 601–610,1998. ##J. Kennedy, “The particle swarm: Social adaptation of knowledge”, in Proc. Int. Conf. Evolutionary Computation, Indianapolis, IN, pp. 303–308, Apr. 1997. ##J. Kennedy, “Methods of agreement: Inference among the eleMentals”, in Proc. 1998 IEEE Int. Symp. Intelligent Control, pp.883–887, Sept. 1998. ##Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm adaptation”, in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Berlin, Germany: SpringerVerlag, pp. 591–600, 1997. ##M. Clerc and J. Kennedy, “The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 58–73, Februrary 2002. ##R. Storn and K. Price, “Differential evolutiona simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, Vol. 11, No. 4, pp. 341–359, 1997. ##J. Ionen, J. K. Kamarainen and J. Lampinen, “Differential evolution training algorithm for feedforward neural networks”, Neural Process Letters, Vol. 17, No. 1, pp. 93–105, 2003. ##R. Storn, “Designing nonstandard filters with differential evolution”, IEEE Signal Processing, Vol. 22, No. 1, pp. 103–106, 2005. ##R. Joshi and A. C. Sanderson, “Minimal representation multisensory fusion using differential evolution”, IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 29, No. 1, pp. 63–76,1999. ##A. R. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization”, Ecol. Inform., Vol. 1, No. 4, pp. 355–366, Dec. 2006. ##S. Karimkashi and A. A. Kisk, “Invasive Weed Optimization and its Features in Electromagnetics”, IEEE Transactions on Antennas and Propagation, Vol. 58, No. 4, pp. 1269–1278, Apr. 2010. ##M.I.K.M. Safari, N.Y. Dahlan, N.S. Razli and T.K.A. Rahman, “Electricity Prices Forecasting Using ANN Hybrid with Invasive Weed Optimization (IWO)”, IEEE 3rd International Conference on System Engineering and Technology (ICSET), pp. 275–280, Aug. 2013. ##A. Ghosh, A. Chowdhury, R. Giri, S. Das and A. Abraham,“A hybrid evolutionary direct search technique for solving Optimal Control problems”, 10th international Conference on Hybrid Intelligent Systems (HIS), pp. 125–130, Aug. 2010. ##R. Giri, A. Chowdhury, A. Ghosh, S. Das, A. Abraham, V. Snasel, “A Modified Invasive Weed Optimization Algorithm for training of feedforward Neural Networks”, IEEE International Conference on System Man and Cybernetics (SMC), pp. 3166–3173, Oct. 2010. ##H. William, W. Wolberg, N. Street and O. L. Mangesarian, 1992, UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html##]
Comparing AODV and ADV Routing Protocols in Urban Environment
2
2
In vehicular ad hoc networks there is the ability to communicate between vehicles. This Communication which is wireless should be rapid and reliable. These networks have unique characteristic. This communication among vehicles has many advantages in safety and comfort applications. Since, the roads are always encountered to accidents and risks, using optimized tools could be useful in these situations. In this study, AODV and ADV routing algorithms has been compared through their performance metrics in a city scenario, include delay, network loss, collision and throughput were investigated through NCTUns 6.0.
1

55
59


Effat
Jazayerifar
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran
Electrical Engineering Department, Islamic
Iran
jazayerifar.e@gmail.com


Reza
SabbaghiNadoshan
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran.
Electrical Engineering Department, Islamic
Iran
r_sabbaghi@iauctb.ac.ir


Shahram
Javadi
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran.
Electrical Engineering Department, Islamic
Iran
sh.javadi@iauctb.ac.ir
Vanet
Routing Protocols
City environment
Safety Applications
[B. T. Sharef, R. A. Alsaqour, and M. Ismail, "Vehicular communication ad hoc routing protocols: A survey," Journal of network and computer applications, Vol.40, pp.363396, Apr. 2014. ##S. Zeadally et al., "Vehicular ad hoc networks (VANETS): status, results,and challenges," Telecommunication Systems, Vol.50, No.4, pp.217241, 2012. ##Y. Kumar, P. Kumar, and A. Kadian, "A Survey on Routing Mechanism and Techniques in Vehicle to Vehicle Communication (VANET)," International Journal of Computer Science & Engineering Survey (IJCSES), Vol.2, No.1, pp.135143, Feb. 2011. ##A. Bachir and A. Benslimane, "A Multicast Protocol in Ad hoc Networks A Multicast Protocol in Ad hoc Networks," in 57th IEEE Semiannual Vehicular Technology Conference, Vol.4, pp.24562460, 2003. ##S. I. Chowdhury et al., "Performance Evaluation of Reactive Routing," in 17th AsiaPacific Conference on Communications (APCC), Sabah, pp.559564, 2011. ##R. Anggoro et al., "Performance Evaluation of AODV and AOMDV with Probabilistic Relay in VANET Environments," in Third International Conference on Networking and Computing, Okinawa, pp.259263, 2012. ##B. Ding et al., "An Improved AODV Routing Protocol for VANETs," in Internatioal Conference On Wireless Communication And Signal Processing, Nagapattinam, Tamil Nadu, pp.15, 2011. ##O. Abedi, M. Fathy, and J. Taghiloo, "Enhancing AODV Routing Protocol Using Mobility Parameters in VANET," in International Conference On Computer Ssystems And Applications, Doha, pp.229235, 2008. ##J. Ledi et al., "AODV enhancements in a realistic VANET context," in Internatoinal Conference On Wireless Communication in Unusual And Confined Areas, Clermont Ferrand, pp.15, 2012. ##JM. U. Kim and K. Y. Yoon, "Fast Path Recovery Scheme for V2V Communications using AODV," in International Conference On Information Scince And Applications(ICISA), Suwon, pp.12, 2013. [11] C. E. Perkins and E. M. Royer, "Adhoc OnDemand Distance Vector Routing," in Second IEEE Workshop On Mobile Computing System And Applications, New Orleans, LA, pp.90100, 1999. [12] R. V. Boppana and S. P. Konduru, "An Adaptive Distance Vector Routing Algorithm for Mobile ad Hoc Networks," in Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, Anchorage, AK, pp.17531762, 2001. [13] S. Y. Wag et al., "NCTUns 4.0: An Integrated Simulation Platform for Vehicular Traffic, Communication, and Network Researches," in IEEE 66th vehicular Technology Conference, Baltimore, MD, pp.20812085, 2007.##]