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objective function

در نشریات گروه فنی و مهندسی
  • سید وحید زیارت نیا، سید عابد حسینی *

    استفاده از سیستم های تولید پراکنده (DG) تاثیرات به سزایی ازجمله افزایش پایداری پروفیل ولتاژ، کاهش تلفات توان و حل مشکلات مربوط به پایداری ولتاژ دارد. این مقاله به ارائه تابع هدف به منظور جایابی و تعیین اندازه بهینه در سیستم های DG در راستای بهینه سازی چندمنظوره نظیر افزایش پروفیل ولتاژ، کاهش اتلاف توان و صرفه اقتصادی به کمک الگوریتم فرا ابتکاری بهینه ساز حسابی (AOA) می پردازد. در روش پیشنهادی، برای تعیین محل و اندازه بهینه در سیستم های DG از رویکردهای کمترین تلفات و بهبود سطح پروفیل ولتاژ پس از توان تزریقی به سیستم در شبکه های توزیع و فوق توزیع استفاده می شود. این پژوهش بر روی یک شبکه 33 گذرگاه IEEE به کمک AOA اجرا شده است و نتایج آن با دو الگوریتم ژنتیک (GA) و بهینه ساز ازدحام ذرات (PSO) مقایسه شده است. نتایج نشان می دهد روش پیشنهادی در زمینه جایابی و تعیین اندازه بهینه در سیستم های DG به سبب دارابودن عملگرهای کارآمد و پارامترهای مناسب نسبت به سایر روش های بهینه سازی اشاره شده برتری دارد. به عنوان نمونه روش AOA نسبت به PSO و GA به ترتیب 4/33 و 8/32 درصد سود بیشتری حاصل کرده است. به طور ویژه نتایج نشان می دهد AOA از سرعت بالاتر در همگرایی و یافتن مکان بهینه در سیستم-های DG برخوردار است.

    کلید واژگان: الگوریتم بهینه ساز حسابی، سیستم تولید پراکنده، افزایش پروفیل ولتاژ، تابع هدف.
    Seyyed Vahid Ziaratnia, Seyyed Abed Hosseini *

    The use of distributed generation (DG) systems has significant effects, such as increasing the stability of the voltage profile, reducing power losses, and solving problems related to voltage stability. This study proposes an objective function to locate and determine the optimal size in DG systems. The objective function is based on increasing the voltage profile, reducing power loss, and improving economic efficiency using an arithmetic optimization algorithm (AOA). The proposed approach uses the lowest losses and improvement of the voltage profile level after injecting power into the system in distribution networks and sub-transmission networks to determine and locate the optimal size in DG systems. This research has been implemented on an IEEE 33-bus network using AOA, and the results are compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the proposed approach is superior to other optimization methods in locating and determining the optimal size, power loss, and cost function in DG systems. For example, AOA has obtained more profit than PSO and GA, 33.4% and 32.8%, respectively. In particular, the results show that AOA has a higher convergence speed in finding the optimal location in DG systems.

    Keywords: Arithmetic Optimization Algorithm, Distributed Generation System, Voltage Profile Increase, Objective Function
  • فروزان رشیدی، صمد نجاتیان، حمید پروین*، وحیده رضایی، کرم الله باقری فرد

    خوشه بندی داده ها یکی از وظایف اصلی داده کاوی است که وظیفه کاوش الگوهای پنهان در داده های بدون برچسب را بر عهده دارد. به خاطر پیچیدگی مسیله و ضعف روش های خوشه بندی پایه، امروزه اکثر مطالعات به سمت روش های اجماع خوشه بندی هدایت شده است. اگر چه برای بیشتر مجموعه داده ها، الگوریتم های خوشه بندی منفردی وجود دارد که نتایج قابل قبولی به دست می دهند، اما توانایی یک الگوریتم خوشه بندی منفرد محدود است. در واقع هدف اصلی اجماع خوشه بندی جستجوی نتایج بهتر و پایدارتر، با استفاده از ترکیب اطلاعات و نتایج حاصل از چندین خوشه بندی اولیه است. در این مقاله، روشی مبتنی بر اجماع خوشه بندی پیشنهاد خواهد شد که مانند بیشتر روش های انباشت شواهد دارای دو گام است: 1- ساختن ماتریس مشارکت همزمان و 2- تعیین افراز های نهایی از ماتریس مشارکت پیشنهادی. در روش پیشنهادی، برای ساخت ماتریس مشارکت همزمان، علاوه بر هم خوشه بودن نمونه ها از بعضی اطلاعات دیگر هم استفاده خواهد شد. این اطلاعات می توانند مربوط به میزان شباهت نمونه ها، اندازه خوشه های اولیه، میزان پایداری خوشه های اولیه و غیره باشد. در این مقاله مسیله خوشه بندی به صورت یک مسیله بهینه سازی صریح توسط مدل آمیخته گوسی تعریف می شود و که با استفاده از الگوریتم آبکاری فلزات حل می شود. همچنین روشی تکاملی مبتنی بر آبکاری فلزات برای تعیین افراز نهایی از ماتریس مشارکت همزمان پیشنهادی ارایه خواهد شد. مهم ترین بخش روش تکاملی، تعیین تابع هدفی است که تضمین کند افراز نهایی از کیفیت بالایی برخوردار خواهد بود. نتایج تجربی نشان می دهد روش پیشنهادی از نظر معیارهای مختلف ارزیابی کیفیت خوشه بندی از سایر روش های مشابه بهتر می باشد.

    کلید واژگان: اجماع خوشه بندی، مدل آمیخته گوسی، الگوریتم آبکاری فلزات، ماتریس مشارکت همزمان، پایداری، تابع هدف
    Froozan Rashidi, Samad Nejatian, Hamid Parvin*, Vahideh Rezaei, Karamolah Bagheri Fard

    Data clustering is one of the main tasks of data mining, which is responsible for exploring hidden patterns in unlabeled data. Due to the complexity of the problem and the weakness of the basic clustering methods, today most of the studies are directed towards clustering ensemble methods. Although for most datasets, there are individual clustering algorithms that provide acceptable results, but the ability of a single clustering algorithm is limited. In fact, the main purpose of clustering ensemble is to search for better and more stable results, using the combination of information and results obtained from several initial clustering. In this paper, a clustering ensemble-based method will be proposed, which, like most evidence accumulation methods, has two steps: 1- building a simultaneous participation matrix and 2- determining the final output from the proposed participation matrix. In the proposed method, some other information will be used in addition to the clustering of the samples to construct the simultaneous participation matrix. This information can be related to the degree of similarity of the samples, the size of the initial clusters, the degree of stability of the initial clusters, etc. In this paper, the clustering problem is defined as an explicit optimization problem by the mixed Gaussian model and is solved using the simulated annealing algorithm. Also, an evolutionary method based on simulated annealing will be presented to determine the final output from the proposed simultaneous participation matrix. The most important part of the evolutionary method is to determine the objective function that guarantees the final output will be of high quality. The experimental results show that the proposed method is better than other similar methods in terms of different clustering quality evaluation criteria.

    Keywords: Clustering ensemble, Gaussian mixture model, simulated annealing algorithm, simultaneous participation matrix, stability, objective function
  • Mehdi Shalchi Tousi, * Samane Laali

    This paper presents an economical optimization for cost and weight of reinforcement cantilever concrete retaining walls using Cuckoo Optimization Algorithm (COA). The proposed optimization algorithm is inspired from the life of a bird family called cuckoo. The capability of this algorithm is compared with other optimization methods available in the literature including ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), firefly algorithm (FA), and cuckoo search (CS). A computer program has been developed by using the COA method for optimizing retaining walls. Five types of retaining walls were considered and sensitivity analyses were performed to find out the role of important parameters such including stem height, surcharge, backfill slope, and backfill unit weight and friction angle. Also, Coulomb and Rankine methods are used to estimate lateral earth pressures. The results show that the COA can minimize retaining walls from both cost and weight viewpoints. In addition, the COA can achieve to better results than ACO, BFOA, PSO, APSO, FA, and CS. The performed sensitivity analysis illustrates that with increasing surcharge and stem height, the cost and weight of wall increase. Also, the cost and weight objective functions decrease with increasing the soil unit weight. In addition, the Coulomb method gives lower cost and weight quantities than the Rankine method.

    Keywords: Sensitivity Analysis, Retaining Walls Optimization, Cuckoo Optimization Algorithm, Objective Function, Optimum Design
  • Mehdi Shalchi Tousi, Samane Laali *
    This paper presents an economical optimization for cost and weight of reinforcement cantilever concrete retaining walls using Cuckoo Optimization Algorithm (COA). The proposed optimization algorithm is inspired from the life of a bird family called cuckoo. The capability of this algorithm is compared with other optimization methods available in the literature including ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), firefly algorithm (FA), and cuckoo search (CS). A computer program has been developed by using the COA method for optimizing retaining walls. Five types of retaining walls were considered and sensitivity analyses were performed to find out the role of important parameters such including stem height, surcharge, backfill slope, and backfill unit weight and friction angle. Also, Coulomb and Rankine methods are used to estimate lateral earth pressures. The results show that the COA can minimize retaining walls from both cost and weight viewpoints. In addition, the COA can achieve to better results than ACO, BFOA, PSO, APSO, FA, and CS. The performed sensitivity analysis illustrates that with increasing surcharge and stem height, the cost and weight of wall increase. Also, the cost and weight objective functions decrease with increasing the soil unit weight. In addition, the Coulomb method gives lower cost and weight quantities than the Rankine method.
    Keywords: Retaining walls optimization, Sensitivity analysis, Cuckoo Optimization Algorithm, Objective function, Optimum design
  • Vahab Montazeri *, Atefeh Ghazi

     It is essential to separate two immiscible liquids from gas to produce the light liquid, heavy liquid, and vapor phases. The separation of water from hydrocarbons is a practical example in the oil industry. For such separation in industry, three phase separator is used. In this study, different parameter and the weight of the three-phase separator was optimized with the genetic algorithm (G.A.) and finally, the total cost of manufacturing the separator was decreased. Different types of three-phase separators are vertical, horizontal, and spherical. The separator works in the operating condition of 172 kPa and 445 K, respectively and the real weight of the separator is 8131 kg. For the optimization target, the flow of vapor, light liquid, and heavy liquid was considered constant during the optimization process. The objective function (O.F.)  is obtained from the weight of the separator and 3 multiparameter equations. Also, 7 parameters which include: separator aspect ratio (L/D), the height of heavy liquid (HHL), height of light liquid (HLL), hold-up time (TH), surge time (TS), low liquid level (HLLL) and vapor level (Hv) are used in G.A. as constraints. The weight of the optimized separator was calculated 6001 kg approximately. So, with this method, the total weight of the separator decreases by about 26.2 % as compared to the real weight of the separator. On the other hand, the maximum difference between the answers was 3.3%, which is acceptable.  Also, error analysis of the predicted results is calculated by mean absolute percentage error (MAPE) for 7 design parameters of the three-phase separator and separator weight, which are in an acceptable level of accuracy. The presented approach can have potential application for the development of low-cost manufacturing of three-phase separators in the petroleum industry.

    Keywords: optimization, Three-phase separator, Genetic Algorithm, Petroleum, Objective Function
  • یدالله شفیعی، فرامرز فقیهی*، امیرحسین سالمی، حسین حیدری

    ایزولاتورهای قابل نصب در فضاهای آزاد معمولا در معرض آلودگی هوا و شرایط سخت جوی می باشند. بنابراین مطالعه مشخصات تخلیه ای آنها در چنین شرایطی الزامی است. بهنیه سازی پارامترهای پوسته ایزولاتورهای پلیمری باهدف کمینه نمودن جریان نشتی و شدت میدان الکتریکی موضوع این مقاله است. در جهت رسیدن به این هدف از یک مدل ریاضی ساده استفاده شده و نتایج حاصل از حل آن با خروجی های بعضی تست های آزمایشگاهی در مورد پارامترهای موثر در شکل ظاهری ایزولاتورهای پلیمری مقایسه شده اند. به طور ویژه مقادیر بهینه برای فاصله بین کلاهک ها، زاویه آنها، و نسبت بین شعاع آویختگی به فاصله بین چترک ها مورد بررسی قرارگرفته اند. برای طرح مساله به طور مشخص ابتدا تابع هدفی با استفاده از یک نمونه برش عمودی (پروفایل) از یک ایزولاتورپلیمری موجود در منابع معتبر مشخص گردید. سپس با لحاظ نمودن توصیه های استاندارد IEC، محدودیت های لازم نیز اضافه شد. پس از استخراج بهترین جواب ها، در نهایت نیز از یک شبیه سازی کامپیوتری بر مبنای روش المان محدود "FEM" برای نشان دادن میزان کاهش جریان نشتی و شدت میدان الکتریکی ناشی از کاربرد طرح پیشنهادی استفاده گردید.

    کلید واژگان: پارامترهای پوسته، ایزولاتورهای پلیمری، بهینه سازی، زاویه چترک ها، تابع هدف
    Yadollah Shafiei, Faramarz Faghihi*, AmirHossein Salemi, Hossein Heydari

    Outdoor insulators are usually exposed to air pollution and severe weather conditions. Therefore, studying the discharge characteristics of insulators in such conditions is manditory. Optimization of polluted composite insulators shed parameters to minimize leakage current and electric field inlensity is the subject of this paper.Validity of the results extracted from a mathematical optimization model has been checked by experimental out comes to obtain reliable values for effective parameters. In particular, optimal values for shed spacing, shed angle, and the ratio of shed overhang to shed spacing have been investigated. Inorder to reach the above- mentioned goals, an optimization tool was implemented. To set up the problem, an objective function was developed, using an existing profile for a polymeric insulator in literature. Consequently, the constraints, recommended by IEC standard were added to the model. Finally, a computer simulation was performed, based on finite elements method ((FEM)) to visualize leakage current and electric field intensity reduction, resulted from the proposed design.

    Keywords: shed parameters, polymeric insulators, optimization, objective function, shed angle, shed spacing
  • Fatemeh Heydari Pirbasti, Mahmoud Modiri, Kiamars Fathi-Hafshejani, Alireza Rashidi-Komijan

    With the expansion of human activities, the volume of waste and hazardous waste produced has increased dramatically. Increasing the volume of waste has created challenges such as transportation hazards, cleanup, disposal, energy consumption, and most impor tant environmental problems. The difficulty of unsafe waste control is one of the critical studies topics. Finding the o ptimal location of hazardous waste disposal is one of the issues that, if done properly, can significantly reduce the aforementioned challenges. The increasing volume of information, the complexity of multivariate decision criteria, have led to the lack of conventional methods for finding the optimal location. Machine learning methods have proven to be effective and superior in many areas. In this paper, a new method based on machine learning for finding the optimal location of hazardous waste disposal is p resented . In the proposed method, after applying clustering in the separation of the desired areas, the gray wolf algorithm optimization is used to find the optimal location of waste disposal . In order to apply the gray wolf optimization algorithm, a multi variate target function is defined . Cluster centers as were chosen as location of waste disposal . Proposed method is performed on collected data from the study area in Iran, Tehran province . Proposed clustering method is evaluated and compared withs some metaheuristics algorithm. The simulation results of the proposed method show cost reduction in finding the desired locations compared to similar researches . Also, Xi and Separation index used for evaluation of proposed clustering method to select the best location. The number of best locations using Xi and Separation index claim the superiority of the proposed method

    Keywords: Waste Disposal, Machine Learning, Clustering, Gray Wolf Optimization Algorithm, Objective Function
  • F. Biabani, A. Razzazi, S. Shojaee*, S. Hamzehei-Javaran

    Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving structure-related problems is presented. The main purpose of this paper is to present an algorithm that addresses the major drawbacks of commonly-used algorithms including the Grey Wolf Optimization Algorithm (GWO), the Gravitational Search Algorithm (GSA), and the Particle Swarm Optimization Algorithm (PSO), and at the same time benefits from a high convergence rate. Also, another advantage of the proposed CGPGC algorithm is its considerable flexibility to solve a variety of optimization problems. To this end, we were inspired by the GSA law of gravity, the GWO's top three search factors, the PSO algorithm in calculating speed, and the cellular machine theory in the realm of population segmentation. The use of cellular neighborhood reduces the likelihood of getting caught in the local optimal trap and increases the rate of convergence to the global optimal point. Achieving reasonable results in mathematical functions (CEC 2005) and spatial structures (with a large number of variables) in comparison with those from GWO, GSA, PSO, and some other common heuristic algorithms shows an enhancement in the performance of the introduced method compared to the other ones.

    Keywords: truss optimization, CGPGC, grey wolf optimizer, gravitational search algorithm, particle swarm optimization, objective function, CEC functions
  • Mohsen Hajabdollahi, Mohammad Shafiey Dehaj *, Hassan Hajabdollahi

    In this study, five different heat exchangers (HE) including, plate-fin (PFHE), fin tube (FTHE), rotary regenerator (RR), shell and tube (STHE) and gasket plate (GPHE) are optimized using four different algorithms including the binary genetic algorithm (BGA), real parameter genetic algorithm (RGA), particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. Verified codes are used for all heat exchangers and total annual cost (TAC) is considered as the objective function and heat exchanger configuration parameters are chosen as design parameters in all studied exchangers. RGA has the lowest insensitivity to the algorithm input parameters, or lowest relative standard deviation (RSD), for all studied heat exchangers. The best TAC in the GPHE, FTHE, PFHE, RR, STHE can be achieved in the points <C1, C2> = (0,0.6), (0, 1.95), (0, 1.5), (0, 2.1), (0, 1.65) and < , > = (2.4,2.4), (1, 2.4), (3.25, 3.75), (3.15, 3), (2.6, 2.8) where  the lowest run- time and RSD are our basic requirements, respectively. The results also reveal that DE has the worst result in the case of RSD and GA has the worst result in the case of run-time. Finally, RGA is recommended for the optimization of different types of heat exchangers.

    Keywords: Different Types of Heat Exchangers, Optimization Algorithms, Objective Function, AlgorithmOperating Parameters
  • A. Ghadimi Hamzehkolaei*, A. Vafaeinejad, G. Ghodrati Amiri

    This paper presents an optimization-based model updating approach for structural damage detection and quantification. A new damage-sensitive objective function is proposed using a condensed form of the modal flexibility matrix. The objective function is solved using Chaotic Imperialist Competitive Algorithm (CICA), as an enhanced version of the original Imperialist Competitive Algorithm (ICA), and the optimal solution is reported as the damage detection results. The application of the CICA in vibration-based damage detection and quantification has been successfully investigated in a feasibility study published by the authors of the present paper and herein, its application is generalized for a case in which a complex (but more sensitive) objective function is utilized to formulate the damage detection problem as an inverse model updating problem. The method is validated by studying different damage patterns simulated on three numerical examples of the engineering structures. Comparative studies are carried out to evaluate the accuracy and repeatability of the proposed method in comparison with other vibration-based damage detection methods. The obtained results introduce the proposed damage detection approach as a robust method with high level of accuracy even in the presence of noisy inputs.

    Keywords: finite element model, structural damage, condensed modal flexibility, objective function, chaotic imperialist competitive algorithm (CICA)
  • A. Contreras *
    This work proposes an objective function to optimize an ultra wideband antenna for adjusting the bandwidth and coupling with other elements, based on the performance comparison of several objective functions from the literature. The optimal dimensions of a printed rectangular monopole antenna were obtained with the Particle Swarm Optimization method to compare such functions. In the results of the comparison, the linear functions had a mean value of S11 magnitude near the threshold, but they presented a smaller standard deviation than the rest of the functions. The logarithmic and cubic functions showed a mean value of S11 magnitude higher than the double of the threshold, but they had superior standard deviation values, which did not happen with the quadratic function. Hence, the proposed function is the mean of a logarithmic expression with the quadratic argument. With this function, a bandwidth adjustment of 130%, a mean S11 magnitude of -22.1 dB and a standard deviation equal to 6.7 dB were obtained on the resonant band for the designed antenna. In this way, the proposed function can be used to avoid interference with other wireless systems and to obtain a uniform coupling of the antenna.
    Keywords: Objective Function, ultra wideband antenna, Particles Swarm Optimization, Bandwidth Adjustment, S11 Magnitude
  • رضا مویدفر*، میلاد بهاروندی

    مدیریت تعمیر و نگهداری روسازی یک فرآیند هماهنگ و منظم برای انجام تمام فعالیت های مربوط به فراهم سازی و نگهداری روسازی جاده ها می باشد. هدف عمده مدیریت روسازی ، پیش بینی شرایط روسازی و هزینه مرتبط با نگهداری و بازسازی در یک برنامه زمان بندی مشخص و کمک به برنامه ریزی کارهاست [1]. با ساخت و اجرای صحیح مدیریت رو سازی ، امکان تصمیم گیری های صحیح ، آگاهانه و پایدار در نگهداری، ترمیم و بازسازی ها فراهم می آید . مدیریت تعمیر و نگهداری روسازی راه ها ، روشی اقتصادی و به صرفه برای مراقبت از یک سرمایه ملی بسیار مهم و برنامه ریزی برای حفاظت و بهسازی آن است [2]. در این پژوهش هدف آن ا ست که با توجه به عدم قطعیت های موجود در میزان بودجه های تخصیص یافته جهت تعمیر و نگهداری روسازی جاده ، مدیریت بهینه صورت می گیرد، ارایه یک روش مناسب برای صرف این بودجه به بهترین نحو ممکن است. در این پژوهش ،محور خرم آباد - درود به عنوان یک مطالعه موردی انتخاب شده است، که جهت مدیریت بهینه تعمیر و نگهداری روسازی جاده ها تحت عدم قطعیت بودجه از روش برنامه ریزی خطی استفاده شده است. تابع هدف نهایی این برنامه ریزی ، ارتقای سطح کیفی راه های استان با داشتن محدودیت بودجه ای است. برای انجام محاسبات از نرم افزار GAMS استفاده شده است ، نتایج نشان خواهد داد که بودجه در دسترس چگونه بین بخش های گوناگون تقسیم نمود تا بهترین نتیجه ممکن حاصل شود.

    کلید واژگان: مدیریت تعمیر و نگهداری، عدم قطعیت بودجه، ایمنی راه ها، تابع هدف
    Reza Moayedfar *, Milad Baharvandi

    pavement management system (PMS) is the coordinate and regular process for doing all of the activities in roads. the main purpose of PMS is the prediction of pavement condition and the life cycle cost for determining the suitable time table. by true construction and management, we can provide a true decision making and sustainable maintenance for rehabilitation and reconstruction of the roads. The true construction and management. PMS is the economic method for maintenance of the one of the most important national funds. the aim of this research is that according to the uncertainty of the assigned budgets, the optimum management for M&R methods was done. at this research, the road of Khorramabad-dorood was selected as the case study. the objective function at this research is the promotion of the roads quality according to budget limitations. for calculations, GAMS software has been used. the results show that, how we can prioritize the budget among the several sections.

    Keywords: maintenance, rehabilitation management, budget’s uncertainty, roads safety, Objective Function
  • علی کاوه*، سید میلاد حسینی، فرناز برزین پور

    در مطالعه ی حاضر، یک روش جدید به روزرسانی مدل بر مبنای پارامترهای مودال اصلی سازه (بسامدهای طبیعی و شکل های مودی متناظر) ارایه شده است. بدین منظور، یک تابع ترکیبی ارتعاش محور با هدف کمینه سازی اختلاف بین مشخصات سازه ی اندازه گیری شده و مدل تحلیلی تعریف شده است. به منظور کاهش آثار نوفه، یک تابع جریمه بر تابع هدف اعمال شده است. برای حل مسیله ی شناسایی آسیب از الگوریتم بهینه یاب مبتنی بر آموزش و یادگیری استفاده شده است. جهت ارزیابی تابع هدف، سه مثال عددی بررسی شده است. چالش هایی نظیر اثر نوفه و تابع جریمه در نتایج شناسایی آسیب مطالعه شده است. همچنین مطالعه یی برای مقایسه ی تابع هدف پیشنهادی با سه تابع هدف دیگر مبتنی بر اطلاعات مودال انجام شده است. نتایج نشان می دهند که روش پیشنهادی با اعمال تابع جریمه و به کار بردن الگوریتم بهینه سازی مبتنی بر آموزش و یادگیری می تواند یک روش قابل اطمینان و پایدار در شناسایی آسیب سازه ها محسوب شود.

    کلید واژگان: شناسایی آسیب، روش به روزرسانی مدل، پارامترهای مودال، تابع هدف، الگوریتم بهینه سازی مبتنی بر آموزش و یادگیری
    A. Kaveh *, S.M. Hosseini, F. Barzinpour

    Engineering structures are prone to damage over their service life as a result of natural disaster so that damage spreading may lead to many casualties. In order to prevent these catastrophic events, early damage detection must be carried out. By considering these issues, numerous structural damage detection methods have been proposed by many researchers in the last few decades. Among all sorts of methods developed for damage detection in structures, vibration-based methods due to their simplicity and applicability are highly favored by many researchers. The basic conceptual of the vibration-based methods is that modal parameters (natural frequencies and their associated mode shapes) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause changes in the modal properties. A class of vibration-based methods is identified and damages are quantified using the model updating approach. In these methods, an objective function defined in terms of the discrepancies between the analytical model and real structural system is minimized as an optimization problem. In this paper, a novel model updating method is presented based on a structure’s main modal parameters (natural frequencies and their corresponding modal shapes). For this purpose, a hybrid vibration-based objective function is proposed to minimize the differences between the structure’s properties and the analytical model. A penalty function is integrated into the objective function to reduce the effects of noise in damage detection and uncertainties in the assessment procedure. The Teaching-Learning-Based Optimization (TLBO) algorithm is applied to solve this problem as an optimization problem. This algorithm is inspired by the traditional learning process of students in school. The two main stages of this algorithm are the effect of the teacher’s knowledge on student learning by the convergence strategy and students learning from each other by the divergence strategy. To evaluate the applicability of the proposed objective function in detecting the location and intensity of the damage, three numerical cases are considered. These cases include an 8-story shear frame, a continuous beam, and a spatial truss. Different challenges such as the effect of noise on measured data and the effect of the penalty-function on results of damage detection were considered. Furthermore, a comparative study is investigated between the proposed objective function and three other objective functions developed based the main model parameters. The results demonstrated that the proposed method is a reliable and stable technique in damage prognosis in structures.

    Keywords: Damage detection, model updating method, modal parameters, objective function, Teaching–learning-based optimization
  • Hossein Chehardoli*, Ali Ghasemi, Mohammad Daneshyian

    A new safe optimal consensus procedure is presented to guarantee the asymptotic and string stability as well as crash avoidance of large-scale non-identical traffic flow. Since time delay is an inherent characteristic of physical actuators and sensors, measurement delay and lags are involved in the upper level control structure. A third-order linear model is employed to define the 1-D motion of each automated vehicle (AV) and the constant time headway plan is employed to regulate the inter-AV distance. It is assumed that the network structure is decentralized look ahead (DLA) and each AV has access to relative position and velocity regarding with the front AV. A linear control law is introduced for each AV and by performing the stability analysis in frequency domain, the necessary conditions guaranteeing string stability and crash avoidance for large-scale traffic flow are derived. Afterwards, to calculate the optimal control parameters guaranteeing the best performance, an objective function combining all mentioned conditions as well as maximum overshoot, settling time and stability margin is introduced. The genetic algorithm (GA) technique is employed to optimize the presented objective function and obtain the optimal control parameters. Various numerical results are proposed to demonstrate the efficiency of this method.

    Keywords: Large-scale traffic flow, Asymptotic stability, String stability, Crash avoidance, Objective function, Genetic algorithm
  • Mohammadreza Mashayekhi *, H. E. Estekanchi, A. Vafai
    Endurance Time (ET) method is a dynamic analysis procedure in which increasing excitations are imposed on structures; these excitations are known as Endurance Time excitation functions (ETEF). This study presents a method to find the optimal objective function for simulating ETEFs which unconstrained optimization problems are. In optimization problems, equations are defined in term of an objective function. In the problem of simulating ETEFs, the objective function can be defined in many different ways regarding considered intensity measures and respective weighting factors. In addition, the type of calculating residuals (absolute way or relative way) diversifies objective function definitions. The proposed method for determining optimal objective function includes quantifying the accuracy of ETEFs in a scalar quantity regardless of their objective functions and introducing an approach to overcome the dependence of results on initial points of optimizations. The proposed method is applied and results are then presented. It is observed that considering only acceleration spectra and calculating residuals in the relative way creates more accurate ETEFs.
    Keywords: Endurance Time method, optimization, objective function, response spectra
  • AliReza Ghanizadeh *, Nasrin Heidarabadizadeh, MohammadJavad Mahmoodabadi

    The main purpose of this work is the comparison of several objective functions for optimization of the vertical alignment. To this end, after formulation of optimum vertical alignment problem based on different constraints, the objective function was considered as four forms including: 1) the sum of the absolute value of variance between the vertical alignment and the existing ground; 2) the sum of the absolute value of variance between the vertical alignment and the existing ground based on the diverse weights for cuts and fills; 3) the sum of cut and fill volumes; and 4) the earthwork cost and then the value of objective function was compared for the first three cases with the last one, which was the most accurate ones. In order to optimize the raised problem, Genetic Algorithm (GA) and Group Search Optimization (GSO) were implemented and performance of these two optimization algorithms were also compared. This research proves that the minimization of sum of the absolute value of variance between the vertical alignment and the existing ground, which is commonly used for design of vertical alignment, can’t at all grantee the optimum vertical alignment in terms of earthwork cost.

    Keywords: Earthwork Volumes, Group Search Optimization (GSO), Objective function, Optimization, Optimum Vertical Alignment
  • S. M. Hosseini, GH. Ghodrati Amiri*, M. Mohamadi Dehcheshmeh

    Civil infrastructures such as bridges and buildings are prone to damage as a result of natural disasters. To understand damages induced by these events, the structure needs to be monitored. The field of engineering focusing on the process of evaluating the location and the intensity of the damage to the structure is called Structural Health Monitoring (SHM). Early damage prognosis in structures is the fundamental part of SHM. In fact, the main purpose of SHM is obtaining information about the existence, location, and the extent of damage in the structure. Since numerous structural damage detection problems can be solved as an inverse problem based on the proposed objective functions by using optimization algorithm, in this paper, related studies are investigated which discussing objective functions based on Modal Strain Energy (MSE) and flexibility methods including Modal Flexibility (MF), and Generalized Flexibility Matrix (GFM). To illustrate the extent of effectiveness of these objective functions based on the above-mentioned modal parameters, an efficiency index called Impact Factor (IF) is defined. Finally, the best objective function is introduced for each numerical case study based on IF by means of evaluating the obtained result.

    Keywords: structural health monitoring (SHM), objective function, modal strain energy (MSE), modal flexibility (MF), generalized flexibility matrix (GFM), impact factor (IF)
  • سیدعلی سیدرزاقی*، بهادر عادل، علی زارع، غلام رضا قدرتی

    در این مقاله یک رویکرد جدید بروزرسانی مدل برای سلامت سنجی و تعیین محل و شدت آسیب در سازه های مهندسی ارائه می گردد. به این منظور، یک تابع هدف حساس به رخداد آسیب برپایه ی تابع خطای مستقیم  با کمک روش انطباق نقطه ای و به کارگیری اطلاعات مودال سازه ی آزمایش شده و مدل تحلیلی آن معرفی می شود. در این تابع هدف، اطلاعات مودال (بسامدهای طبیعی و شکل های مود متناظر) به صورت مستقیم و بدون واسطه ترکیب می شوند که این امر سهولت ارزیابی تابع هدف و حساسیت زیاد آن به رخ داد آسیب را به دنبال دارد. به منظور یافتن جواب بهینه ی مسئله که همان آسیب های شناسایی شده در سازه است، از الگوریتم بهینه یابی پروانه-شعله استفاده می شود. الهام بخش اصلی این الگوریتم، همگرایی مارپیج پروانه ها به سمت شعله های مصنوعی می باشد. بروزرسانی موقعیت پروانه ها نسبت به شعله ها که بهترین جواب های بدست آمده در طول تکرارها می باشند، احتمال همگرایی زودرس به نقاط بهینه ی محلی را کاهش داده، همگرایی الگوریتم به نقطه ی اکسترمم کلی را تضمین می نماید. کارآیی روش پیشنهادی با مطالعه ی سه مثال عددی که شامل یک قاب برشی هفت طبقه، یک تیر ساده و یک خرپای دو بعدی می باشد، ارزیابی می گردد. در این مطالعه هر کدام از سازه ها با روش اجزای محدود مدل سازی شده و آسیب با کاهش سختی در عضوهای آسیب دیده، شبیه سازی می شود. هم چنین اثر وجود نوفه ی تصادفی در داده های ورودی بر روی عملکرد روش پیشنهادی بررسی می شود. نتایج به دست آمده عملکرد خوب و پایدار روش مطرح شده را برای شناسایی آسیب نشان می دهد.

    کلید واژگان: شناسایی آسیب، اطلاعات مودال، تابع هدف، انطباق نقطه ای، بهینه یاب پروانه-شعله
    Seyed Ali Seyed Razzaghi*, Bahador Adel, Gholamreza Ghodrati, Ali Zare

    Structural damage not only changes the dynamic characteristics of the structure, but also it may lead to complete destruction of the structure in some cases. Since early identification of damage can prevent such catastrophic events, structural health monitoring and damage detection has absorbed the attention of the civil, mechanical and aerospace engineers in the last decades. An effective health monitoring methodology not only can provide information about the global serviceability of the monitored structure, but also it can help the engineers to prepare cost-effective rehabilitation programs based on the obtained details about the health of the structure and its members. Different methods have been proposed for structural damage identification and estimation. Vibration-based methods consider the changes in the structural modal parameters, like natural frequencies and associated mode shapes, and/or their derivatives, like modal flexibility and residual force vector, for damage identification and quantification. Considering their acceptable sensitivity to wide-range of structural damages, vibration-based methods are considered as one of the most practical approaches for structural fault prognosis. Employing vibration parameters to define the damage detection problem as a model updating problem, is one of the well-known strategies that can return both the damage location and extent in different types of engineering structures. Such methods can be solved with optimization algorithms to find and report the structural damage in terms of the global extremums of a damage-sensitive objective function.
    In this paper a new model updating approach for health monitoring and damage localization and quantification in engineering structures is presented. At first, a damage-sensitive objective function, which is based on the error function between the modal data of the monitored structure and its analytical model, is proposed. This objective function is formulated by means of the point-by-point matching strategy to minimize the difference between two models. Modal natural frequencies and the associated mode shape vectors are directly fed to the objective function and this can result in an easy assessment methodology to check the convergence rate of the function. Moreover, in such a case, the objective function uses the sensitivity of both these parameters for damage identification. The proposed inverse problem is solved using Moth-Flame Optimization (MFO) algorithm which has been inspired form spiral convergence of moths toward artificial lights. From mathematical point of view, updating the position of the moths with respect to the flames –which are the best solutions obtained during iterations–, reduces the probability of being trapped in the local extremum points and also, ensures the convergence of the algorithm to its global optimal solution. The applicability of the method was evaluated by studying different damage patterns on three numerical examples of engineering structures: a seven-story shear frame, a simple beam with 10 elements, and a planar truss with 29 elements. In all these studies, damages were simulated as reduction in the stiffness matrix of the damaged elements. Different issues, like noise effects, were considered and their impacts on the performance of the proposed method were investigated. Furthermore, comparative studies were carried out to discuss the advantages and drawbacks of the introduced method as well as the employed techniques. The obtained results indicate that the method is an effective strategy for vibration-based damage detection and localization in engineering structures.

    Keywords: Damage identification, Modal data, Objective function, Point-by-point matching strategy, Moth-flame optimization
  • H. Mazaheri, H. Rahami *, A. Kheyroddin
    Structural damage detection is a field that has attracted a great interest in the scientific community in recent years. Most of these studies use dynamic analysis data of the beams as a diagnostic tool for damage. In this paper, a massless rotational spring was used to represent the cracked sections of beams and the natural frequencies and mode shape were obtained. For calculation of rotational spring stiffness equivalent of uncracked and cracked sections, finite element models and experimental test were used. The damage identification problem was addressed with two optimization techniques of different philosophers: ECBO, PSO and SQP methods. The objective functions used in the optimization process are based on the dynamic analysis data such as natural frequencies and mode shapes. This data was obtained by developing a software that performs the dynamic analysis of structures using the Finite Element Method (FEM). Comparison between the detected cracks using optimization method and real beam shows an acceptable agreement.
    Keywords: crack, rotational spring, objective function, optimization, ECBO, bending rigidity
  • میر رضا غفاری رزین، بهزاد وثوقی
    در این مقاله روش کمینه سازی توابع هدف با کمک شبکه های عصبی موجک چند لایه، جهت مدل سازی توموگرافی یونوسفر به عنوان یک روش جدید ارائه شده است. براساس روش توموگرافی، تابع هدفی تعریف گردیده و سپس با کمک شبکه های عصبی موجک چند لایه (WNN) طراحی شده، مقدار این تابع هدف به کمترین میزان خود می رسد. جهت بهینه سازی وزن ها و بایاس ها در شبکه های عصبی، می بایستی از یک الگوریتم آموزش مناسب بهره گرفت. به همین جهت در این مقاله از الگوریتم های آموزش پس انتشار خطا (BP) و بهینه سازی انبوه ذرات (PSO) استفاده شده است. سه روش ترکیبی برای کمینه سازی توابع هدف که جزو نوآوری های اصلی این مقاله است مورد بررسی و آنالیز قرار گرفته است. در روش اول (RMTNN) از شبکه عصبی مصنوعی پرسپترون 3 لایه با الگوریتم آموزش پس انتشار جهت مدل سازی توزیع چگالی الکترونی استفاده شده است. در روش دوم (MRMTNN) یک شبکه عصبی موجک 3 لایه بهمراه الگوریتم آموزش پس انتشار خطا جهت مدل سازی توزیع چگالی الکترونی بکار گرفته شده و نهایتا در ترکیب سوم (ITNN) از شبکه عصبی موجک 3 لایه بهمراه الگوریتم آموزش بهینه سازی انبوه ذرات جهت مدل سازی تغییرات زمان-مکان چگالی الکترونی بهره گرفته شده است. مشاهدات مربوط به شبکه مبنای ژئودینامیک دائمی ایران (32 ایستگاه GPS به همراه یک ایستگاه اندازه گیری مستقیم یونوسفر) جهت آزمون و ارزیابی هر سه ترکیب مورد استفاده قرار گرفته اند. تمامی نتایج بدست آمده از سه روش با اندازه گیری های ایستگاه یونوسوند و مدل هارمونیک‍ های کلاه کروی (SCH) مقایسه شده است. همچنین شاخص های آماری خطای نسبی و مطلق، جذر خطای مربعی میانگین (RMSE)، بایاس، انحراف معیار و ضریب همبستگی برای هر سه روش پیشنهادی این مقاله مورد محاسبه و بررسی قرار گرفته است. آنالیزهای انجام گرفته در مورد روش های RMTNN، MRMTNN و ITNN بیانگر این موضوع است که روش ITNN نسبت به دو روش دیگر دارای سرعت همگرایی بالا به جواب بهینه و همچنین دقت و صحت بالاست. مقایسه های صورت گرفته نشان دهنده بهبود مدل سازی محتوای الکترون کلی توسط روش ITNN به مقدار 5/0 الی 65/5 TECU در منطقه ایران نسبت به مدل های تجربی یونوسفر می باشد. همچنین متوسط ضریب همبستگی 901/0 مابین خروجی های روش ITNN و اندازه گیری های ایستگاه های یونوسوند، حاکی از کارائی بالای روش پیشنهادی این مقاله در مدل سازی تغییرات زمان-مکان چگالی الکترونی است.
    کلید واژگان: توموگرافی یونوسفر، محتوای الکترون کلی، شبکه عصبی مصنوعی، تابع هدف، چگالی الکترونی، GPS، IRI، 2012، RMTNN، MRMTNN، ITNN
    M. R. Ghaffari Razin, B. Voosoghi
    In the last two decades, knowledge of the distribution of the ionospheric electron density considered as a major challenge for geodesy and geophysics researchers. To study the physical properties of the ionosphere, computerized ionosphere tomography (CIT) indicated an efficient and effective manner. Usually the value of total electron content (TEC) used as an input parameter to CIT. Then inversion methods used to compute electron density at any time and space. However, CIT is considered as an inverse ill-posed problem due to the lack of input observations and non-uniform distribution of TEC data. Many algorithms and methods are presented to modeling of CIT. For the first time, 2-dimensional CIT was suggested by Austin et al., (1988). They used algebraic reconstruction techniques (ART) to obtain the electron density. Since, other researchers have also studied and examined the CIT. Although the results of all studies indicates high efficiency of CIT, but two major limitations can be considered to this
    Method
    first, due to poor spatial distribution of GPS stations and limitations of signal viewing angle, CIT is an inverse ill-posed problem. Second, in most cases, observations are discontinuous in time and space domain, so it is not possible determining the density profiles at any time and space around the world.
    In this paper, the method of residual minimization training neural network is proposed as a new method of ionospheric reconstruction. In this method, vertical and horizontal objective functions are minimized. Due to a poor vertical resolution of ionospheric tomography, empirical orthogonal functions (EOFs) are used as vertical objective function. To optimize the weights and biases in the neural network, a proper training algorithm is used. Training of neural networks can be considered as an optimization problem whose goal is to optimize the weights and biases to achieve a minimum training error. In this paper, back-propagation (BP) and particle swarm optimization (PSO) is used as training algorithms. 3 new methods have been investigated and analyzed in this research. In residual minimization training neural network (RMTNN), 3 layer perceptron artificial neural networks (ANN) with BP training algorithm is used to modeling of ionospheric electron density. In second method, due to the use of wavelet neural network (WNN) with BP algorithm in RMTNN method, the new method is named modified RMTNN (MRMTNN). In the third method, WNN with a PSO training algorithm is used to solve pixel-based ionospheric tomography. This new method is named ionospheric tomography based on the neural network (ITNN).
    The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN. The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods.
    The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN. The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods.
    Keywords: Ionospheric Tomography, Total Electron Content, Artificial Neural Network, Objective Function, GPS, IRI-2012, SCH, RMTNN, MRMTNN, ITNN
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