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genetic algorithm(ga)

در نشریات گروه فنی و مهندسی
  • Hamid Mozafari *, Mojtaba Moravej

    This study investigates using composite patch connections to repair cracked composite sheets under tensile stress. Finite element analysis was employed to model and analyze the behavior of the cracked composite sheet in three scenarios: without a patch, with a one-sided patch, and with a two-sided (symmetric) patch. The primary objective was to examine the influence of the composite patch material on the stress distribution, crack growth strain, and overall performance of the repaired sheet. An optimization approach was also developed to design the multilayer composite patch connection with the lowest weight and cost while withstanding the highest possible load. Two optimization algorithms, the Genetic Algorithm and the Colonial Competitive Algorithm, were implemented and compared in this regard. The results demonstrate that using a symmetric, two-sided composite patch connection effectively reduces crack growth and enhances the strength of the repaired sheet. Furthermore, the optimization analysis revealed that the Colonial Competitive Algorithm provided superior performance to the Genetic Algorithm in identifying the optimal design parameters for the composite patch connection. This study contributes to understanding crack behavior in composite sheets and developing cost-effective, weight-efficient repair solutions using composite patch connections. The findings can inform the design and implementation of composite repair techniques in various engineering applications.

    Keywords: Composite Sheet, Crack Repair, Composite Patch Connection, Genetic Algorithm (GA), Colonial Competitive Algorithm (CCA)
  • Hanyeh Zareian, Vahid Baradaran*, Alireza Rashidikomijan

    The automotive industry serves as a cornerstone of Iran's economy, with auto spare parts demand playing a vital role in its transportation infrastructure. Traditional forecasting methods often struggle to capture the intricacies of Iran's dynamic market dynamics, prompting the adoption of advanced computational techniques. This study explores the efficacy of hybrid neural networks, particularly the combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, optimized with genetic algorithm, in forecasting auto spare parts demand. Empirical evaluation demonstrates the superiority of the CNN-LSTM-GA model over traditional algorithms, showcasing its potential to drive operational efficiency and cost-effectiveness in the automotive supply chain. The findings underscore the significance of embracing innovative methodologies and present avenues for future research to explore broader applicability and scalability in diverse contexts.

    Keywords: Auto Spare Parts, Convolutional Neural Networks (Cnns), Long Short-Term Memory (LSTM), Genetic Algorithm (GA)
  • Mohammadreza Ohadi, Mahdi Hedayati*, Reza Effatnejad, Abdolreza Dehghanitafti

    The growing demand for sustainable development and the integration of renewable energy sources emphasize the critical role of energy hubs (EHs) in modern power systems. EHs synergize electrical, cooling, and heating equipment with demand response (DR) programs and renewable energy resources (RERs) to optimize energy management. This study proposes a combined energy system (CES) framework to manage uncertainties in energy sources and demands, including cooling, heating, wind speed, solar irradiation, and energy prices. The objective is to maximize EH profits by integrating DR programs and RERs across various scenarios. The study utilizes the Slime Mould Algorithm (SMA), the Genetic Algorithm (GA), and the Mixed-Integer Linear Programming (MILP) method to compare performance. The optimization focuses on minimizing operational costs while maximizing renewable energy utilization. Without DR integration, SMA generates 1500 kW from photovoltaic (PV) systems and 2000 kW from wind turbines at a cost of $500,000. Conversely, GA generates 1400 kW from PV and 1800 kW from wind turbines, costing $550,000, while MILP results in 1550 kW from PV and 1950 kW from wind turbines at $510,000. With DR integration, SMA reduces costs to $450,000, GA incurs $500,000, and MILP achieves $460,000. DR programs enhance load management, peak shaving, and load shifting, contributing to cost reduction and efficiency. Simulation results confirm SMA's effectiveness in managing energy resources within a microgrid, optimizing renewables, and significantly reducing costs. SMA consistently outperforms GA and MILP, demonstrating superior cost-effectiveness and resource optimization. This study underscores the importance of advanced optimization algorithms like SMA in modern energy systems, offering a reliable and cost-efficient solution for EHs and supporting sustainable energy management and environmental sustainability.

    Keywords: Energy Management, Renewable Energy Sources, Demand Response Programs, Slime Mould Algorithm (SMA), Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA)
  • M. Erkan Kutuk *, L. Canan Dulger
    Dimensional synthesis of mechanisms to trace given points is an important issue in mechanism and machine science. Having no exact solution makes this issue an optimization problem. This study offers an optimization approach for the dimensional synthesis of planar mechanisms. Four-bar mechanisms having one degree of freedom (DOF) are chosen as the configurations. The proposed method is implemented by establishing the objective functions with specified constraints and searching for the results by using an optimization algorithm. Genetic Algorithm (GA) in Optimization Toolbox-Matlab® is selected as a solver. Different types of four-bar mechanisms like crank-rocker and double-crank including different target points are performed. Mechanisms are depicted by resulted parameters and a Matlab® script prepared plays their animations. As a result, it is proved that the mechanisms whose dimensional properties are obtained by the GA solver have a good tracing capability for the desired paths. This study has the property of being a design guide. Its application is not limited to four bar mechanism. Planar mechanisms with different configurations can be easily synthesized by using this technique.
    Keywords: Mechanism synthesis, optimization, genetic algorithm (GA), Four-bar mechanism
  • Hossein Chehardoli*

    In this article, the optimal robust H2 / H∞ control of self-driving car platoons (SDCPs) under external disturbance is investigated. By considering the engine dynamics and the effects of external disturbance, a linear dynamical model is presented to define the motion of each self-driving car (SDC). Each following SDC is in direct communication with the leader. By utilizing the relative position of following SDCs and the leader, the error dynamics of each SDC is calculated. The particle swarm optimization (PSO) method is utilized to find the optimal control gains. To this aim, a cost function which is a linear combination of H2 and H∞ norms of the transfer function between disturbance and target variables is constructed. By employing the PSO method, the cost function will be minimized and therefore, the robustness of the controller against external disturbance is guaranteed. It will be proved that under the presented robust control method, the negative effects of disturbance on system performance will significantly reduce. Therefore, the SDCP is internally stable and subsequently, each SDC tracks the motion of the leader. In order to validate the proposed method, simulation examples will be presented and analyzed.

    Keywords: H2, H∞ norms, Optimal robust controller, Genetic algorithm (GA), Disturbance, stability
  • Payam Abdolmohammadi, Roham Farahani *
    This paper aims to study the appropriate data mining method to extract the rules from a data set and examining the benefits of using the cuckoo algorithm to extract association rules and compare the execution time of the cuckoo algorithm and genetic algorithm (GA). Therefore, an algorithm is proposed that includes two parts: preprocessing and mining. The first part presents the procedures related to the calculation of cuckoo fit values and in the second part of the algorithm, which is the main achievement of this research. Support and confidence The best position can show the least confidence and support.These mining results can be used to continue mining the association rules. The proposed algorithm is based on the cuckoo search. It hides the sensitive relationship rules with a lower time cost and, at the same time, controls the peripheral effects of non-sensitive rules in a better way. This aim is achieved using recurring to the objective function. The GA is set to be the evaluation criterion to show the prominence of the proposed method. In this method, we compare the speed of the cuckoo algorithm with the genetic algorithm, which uses genetic evolution as a problem-solving model. In general, it is an algorithm based on repetition, most of its parts are selected as random processes, and these algorithms are part of the fitting function. It was chosen as a criterion and we paid .It is scientifically proven that the cuckoo algorithm outperforms the GA in the execution time.
    Keywords: Association rules mining, Genetic Algorithm (GA), cuckoo algorithm, sensitive relationship, non-sensitive relationship, Data mining, Association rules, Dataset, time complexity, Performance Improvement
  • بهروز شهریاری *

    در پژوهش حاضر تحلیل اولیه سازه دیسک دوار مورد استفاده در یک موتور توربینی هوایی و بهینه سازی هندسه آن جهت رسیدن به کمترین جرم در کنار اطمینان مناسب انجام شده است. دیسک مورد بحث همگن و تحت بارگذاری های مکانیکی و حرارتی قرار دارد. جهت تحلیل ترموالاستیک دیسک مفروض روابط حاکم با فرض شرایط تنش صفحه ای استخراج و به کمک نرم افزار محاسباتی متلب تحلیل شده است. به منظور بهینه سازی هندسه از روش های بهینه سازی گرادیانی شمارشی (CSA) و غیر گرادیانی الگوریتم ژنتیک (GA) و الگوریتم کلونی زنبور عسل مصنوعی (ABC) استفاده شده است. مقایسه نتایج به ترتیب نشان دهنده کاهش 49/36، 51/39 و 43/36 درصدی جرم اولیه دیسک با روش های بهینه سازی مورد استفاده است. همچنین زمان تقریبی لازم جهت رسیدن به نتایج بهینه سازی در این روش ها به ترتیب برابر با 3500، 120 و 100 دقیقه بوده است. نتایج نشان می دهند که بیشترین میزان بهبود نتایج مربوط به روش غیرگرادیانی الگوریتم  GAاست. همچنین سرعت همگرایی در روش های غیرگرادیانی الگوریتم  GAو ABC در حدود 30 تا 35 برابر بیشتر از روش CSA است. در نتیجه با توجه به زمان بر بودن تحلیل های گرادیانی اهمیت روش های غیر گرادیانی در صرفه جویی زمان و هزینه های محاسباتی نیز بیشتر نمایان شده و با توجه به نتایج استفاده از روش GA به عنوان یک روش دقیق و با سرعت مطلوب در بهینه سازی مسایل دوار در معرض بارگذاری های ترموالاستیک پیشنهاد شده است.

    کلید واژگان: دیسک دوار، بهینه سازی غیر گرادیانی و گرادیانی، الگوریتم ژنتیک، الگوریتم کلونی زنبور عسل مصنوعی
    Behrooz Shahriari

    This paper presents the initial structural analysis and geometric optimization of a rotating disk used in an aircraft turbine engine. The disk is homogeneous and subject to mechanical and thermal loads. The governing equations for the thermoelastic analysis of the assumed disk were derived under the assumption of plane stress conditions and subsequently analyzed using the MATLAB computational software. Three different optimization methods were implemented for the geometric optimization of the rotating disk: The Counting Sort Algorithm (CSA), the non-gradient Genetic Algorithm (GA), and the Artificial Bee Colony algorithm (ABC). The optimization methods resulted in a reduction of the disk mass by 36.49%, 39.51%, and 36.43%, respectively. Also, the approximate time required to reach the optimization results was 3,500, 120, and 100 minutes, respectively. The results show that the non-gradient genetic algorithm (GA) method has the greatest improvement in results. The convergence speed of the non-gradient GA and ABC methods is also about 30 to 35 times faster than the CSA method. As a result, the importance of non-gradient methods in saving time and computational costs has become more evident, due to the time-consuming nature of gradientbased analyses. Based on the results, the use of the GA method has been proposed as an accurate and efficient method for the optimization of rotating problems under thermoelastic loading.

    Keywords: Rotating disk, Gradient, non-gradient optimization, Counting SortAlgorithm (CSA), Genetic Algorithm (GA), Artificial Bee Colony (ABC)
  • M. Mohebbi*, S. Bakhshinezhad

    The semi-active bracing system locks or unlocks the stand-by braces in an on-off mode utilizing a variable stiffness device (VSD). In this paper, the optimal design of a semi-active bracing mechanism and evaluating its performance in mitigating structural vibration under seismic loading have been studied. The optimal stiffness values of the semi-active braces have been determined by solving two optimization problems including minimizing the maximum acceleration and also minimizing the maximum inter-story drift by imposing a constraint on the maximum acceleration. The genetic algorithm (GA) has been applied to solve the optimization problems. To illustrate the design procedure, an eight-story linear shear frame under earthquake record has been considered and the optimal semi-active braces have been designed. In addition, to assess the performance of optimal bracing system under other records which are different from design record in terms of intensity and frequency content, the structure equipped with optimally designed semi-active braces has been tested under several ground motion records. The results show that the optimal semi-active bracing system has simultaneously reduced different responses of the structure although the acceleration reduction has mainly been less compared to the drift reduction.

    Keywords: Semi-active control, variable stiffness bracing mechanism, optimization, genetic algorithm (GA)
  • Solmaz Nopahari, Hossein Gharaee*, Ahmad Khademzadeh

    It is critical to increasing the network throughput on the internet of things with short-range nodes. Nodes prevent to cooperate with other nodes are known as selfish nodes. The proposed method for discovering the selfish node is based on genetic algorithm and learning automata. It consists of three phases of setup and clustering, the best routing selection based on genetic algorithm, and finally, the learning and update phase. The clustering algorithm implemented in the first phase. In the second phase, the neighbor node selected for forwarding the packet in which has a high value of fitness function. In the third phase, each node monitors its neighbor nodes and uses the learning automata system to identify the selfish nodes. The results of the simulation has shown the detection accuracy of selfish nodes in comparison with the existing methods average 10 %, and the false positive rate has decreased by 5 %.

    Keywords: Internet of Thing (IoT), selfish node, Genetic Algorithm (GA), Learning Automata (LA), Detection accuracy (DA), false positive rate (FPR)
  • راضیه سیرانی، محسن ترابیان*، محمدحسن بهزادی، اصغر سیف

    در این مقاله، طراحی آماری- اقتصادی نمودار کنترل بیزی مبتنی بر توزیع پیشگو برای مشاهدات انفرادی از توزیع مشخصه ی کیفی نمایی ارایه شده است. در انجام این کار، دو نوع توزیع پیشین مزدوج و توزیع جفریز در نظر گرفته شده و بر اساس توزیع مشاهدات در فاز اول، توزیع پیشگو تعیین شده است. سپس با استفاده از مدل اقتصادی لورنزن و وانس، یک طرح آماری- اقتصادی برای داده ها به دست آمده است. پارامترهای طراحی بهینه (فاصله ی نمونه گیری، اندازه ی نمونه و حدود کنترل) با استفاده از الگوریتم ژنتیک تعیین شده و تجزیه و تحلیل حساسیت برای مقادیر مختلف پارامترهای مدل انجام گرفته است. نتایج این رویکرد با نتایج مدل کلاسیک مقایسه شده است. بر اساس نتایج، این روش نسبت به روش کلاسیک موثرتر است

    کلید واژگان: نمودار کنترل بیزی، توزیع پیشین، توزیع پیشگو، طراحی آماری- اقتصادی، الگوریتم ژنتیک
    Razieh Seirani, Mohsen Torabian *, Mohammad Hassan Behzad, Asghar Seif

    In this article with title "Economic-statistical design of Bayesian control chart based on the predictive distribution for individual observations with an exponential qualitative characteristic distribution" the economic-statistical design of the Bayesian control chart based on the predictive distribution for individual observations of the exponential qualitative characteristic distribution is presented. In doing this, two types of the conjugate prior distribution and Jeffrey’s distribution are considered, and based on the distribution of observations in phase I, the predictive distribution is determined. Then, using the economic model of Lorenzen and Vance, an economic-statistical design was obtained for the data. Optimal design parameters (sampling distance, sample size, and control limits) were determined using a genetic algorithm and sensitivity analysis was performed for different values of model parameters. The results of this approach have been compared with the results of the classical model. The results show that this method is more effective than the classical method

    Keywords: Bayesian control chart, prior distribution, predictive distribution, economic-statistical design (ESD), Genetic algorithm (GA)
  • Mohammad Hossein Sattarkhan *
    A multi-product system is one of the different types of manufacturing systems, in which a large number of products are produced that complement each other and have interdependence. These types of systems have recently been widely used in various industries. In some types of multi-product manufacturing industries that offer their products as a package, the scheduling of the production of components of each package affects the time it takes to complete the package. Therefore, a new problem has been defined that the primary purpose of its production scheduling, in addition to reducing the completion time of the products, is to make various items forming a package, get ready over a short interval of time and be supplied to the sales unit so that the package can be delivered to the final consumer. The purpose of this paper is to express the problem of production scheduling of multi-product production systems in the form of linear programming. For this purpose, two mathematical models are presented, and their functions are compared. Besides, an efficient genetic algorithm is proposed to solve the problem, which is able to solve the problem in a reasonable time, with acceptable accuracy.
    Keywords: Parallel Lines Production Scheduling, Operations Sequence, Mixed-integer linear programming (MILP), Genetic algorithm (GA)
  • علی باقری، حمیدرضا آقایی، محمد شمسی، محمدمهدی عابدی، حامد هاشمی دزکی*

    تغییر در پیکره بندی شبکه های توزیع فعال یکی از چالش هایی است که طرح های حفاظتی را با چالش مواجه می سازد. در صورتی که حفاظت بهینه تنها بر اساس پیکره بندی پایه و اتصال تمامی منابع تولیدپراکنده با پست های فوق توزیع انجام شود، نقض قیود هماهنگی در پیکره بندی هایی متفاوت با پیکره بندی پایه اجتناب ناپذیر خواهد بود. در این مقاله روش های حفاظتی ترکیبی و تطبیقی با درنظرگیری آرایش های مختلف شبکه پیشنهاد می گردد. در روش ترکیبی، یک گروه تنظیم حفاظتی برای رله های شبکه درنظر گرفته شده و با حل یک مسیله بهینه سازی واحد و درنظرگیری قیود هماهنگی تمامی آرایش های شبکه، تنظیمات بهینه استخراج می گردد. علی رغم مزایای روش ترکیبی، زمان عملکرد رله ها ناشی از کوچک شدن فضای شدنی مسیله بهینه سازی قابل توجه خواهد بود. از اینرو، روش حفاظت تطبیقی با کمک گروه های تنظیمی مختلف و تفکیک مسیله بهینه سازی به چند مسیله و کاهش قیود هماهنگی در این مقاله ارایه می شود. نتایج به دست آمده از پیاده سازی روش های پیشنهادی بر روی شبکه های 8 شین و 30 شین IEEE دلالت بر برتری حفاظت تطبیقی دارد. یکی از دیگر مزایای روش پیشنهادی، بهینه سازی منحنی مشخصه رله های حفاظتی علاوه بر تنظیمات زمانی و جریانی است که در حفاظت های تطبیقی پیشین کم تر مورد توجه قرار گرفته است

    کلید واژگان: شبکه های توزیع فعال، حفاظت ترکیبی، حفاظت تطبیقی، حفاظت بهینه، پیکره بندی های مختلف شبکه، رله های جریان زیاد، انتخاب منحنی مشخصه استاندارد، الگوریتم ژنتیک، DIgSILENT
    Ali Baghery, Hamidreza Aghaei, Mohammad Shamsi, Mohammadmahdi Abedi, Hamed Hashemi-Dezaki*

    The change in the topology of active distribution networks (ADNs) is one of the essential challenges that might affect the protection schemes. The conventional protection schemes based on base topology result in some coordination constraint violations in other topologies due to the outage of upstream substations and distributed generation units. In this article, new combinational and adaptive protection schemes considering all ADNs’ topologies are proposed. In the proposed combinational protection scheme, one setting group is considered for all relays. The relays’ optimal settings are determined by solving one optimization problem, including all constraints corresponding to all topologies. Although the coordination constraint violations are reduced in the combinational protection scheme, the operating time of relays is increased due to the small feasible area of the optimization problem. Hence, proposing an adaptive protection scheme with different setting groups is useful. The number of coordination constraint violations is reduced in the proposed adaptive protection method, while the speed of the protection system is satisfying. Optimal selection of curves for the overcurrent relays is another contribution of this paper that has received less attention in the available research works. The proposed methods are applied to 8-bus and 30-bus IEEE test systems. Test results show that the proposed adaptive function is more appropriate than the combinational one.

    Keywords: Active distribution networks, Combinational method, Adaptive method, Optimal protection, Different topologies, Overcurrent relays, Optimal selection of standard relay curves, Genetic Algorithm (GA), DIgSILENT
  • Hamidreza Ardeshiri Lordejani, Amir Heidari *, Nader Ghods
    The Reverse Osmosis (RO) process is one of the most widely used technologies in the water treatment industry to reduce water hardness. In this paper, the reverse osmosis treatment plant of Sorkheh city (Semnan province, Iran) was modeled by Artificial Neural Network (ANN) model. The ANN model parameters were optimized with the Genetic Algorithm (GA) method to increase ANN model accuracy. The optimization was done by updating the weight and bias, the number of layer neurons, activator functions, and the ANN training equation. The mean relative error of optimized ANN model results with respect to industrial data was obtained about 0.73% for water outlet flow rate and 0.47% for water pH. While the mean relative errors for water outlet flow rate and water pH in the non-optimized ANN model were evaluated 26.24% and 4.76%, respectively. Also, the results showed that the regression coefficient for the optimized neural network is equal to 0.995.
    Keywords: Reverse Osmosis (RO) plant, Artificial Neural Network (ANN) model, Genetic Algorithm (GA), Optimization
  • محمد شمسی، حامد هاشمی دزکی*

    تغییر در آرایش شبکه های توزیع ناشی از خروج یکی از پست های بالادست یا منابع تولید پراکنده، یکی از چالش های اساسی طراحی شبکه های توزیع خواهد بود که تاثیر بسزایی در طرح های حفاظتی و نقض قیود هماهنگی در حالات مختلف بهره برداری خواهد گذاشت. درصورت عدم توجه به پیکربندی های شبکه، بروز نقض قیدهای هماهنگی در سایر پیکربندی های شبکه ناشی از خروج یکی از منابع تولید یا پست های بالادست اجتناب ناپذیر خواهد بود. طرح های حفاظت تطبیقی نسبت به طرح هایی که تنها از یک گروه تنظیم حفاظتی استفاده می نمایند، برتری داشته و سرعت عملکرد بهتری خواهند داشت. از خلاء های تحقیقاتی که کم تر به آن ها توجه شده است، محدودیت در تعداد گروه های تنظیم نسبت به حالات بهره برداری است. رویکرد ارایه شده در این مقاله، حفاظت تطبیقی با درنظرگرفتن آرایش های مختلف شبکه با بهره مندی از قابلیت گروه های تنظیم مختلف رله های اضافه جریان است. با توجه به محدودیت در مورد تعداد گروه تنظیم های موجود در رله های تجاری، در طرح پیشنهادی تنظیمات بهینه ی رله های اضافه جریان برای حالات مختلف شبکه با درنظرگرفتن تعداد محدود گروه های تنظیم رله ها به دست خواهد آمد. یکی از مزایا و نوآوری های روش پیشنهادی، بهینه سازی منحنی مشخصه ی رله های حفاظتی در کنار بهینه سازی تنظیمات زمانی و جریانی است که در طرح های حفاظت تطبیقی پیشین کم تر به آن توجه شده بود. افزایش زمان طرح حفاظت تطبیقی ناشی از اعمال محدودیت در تعداد گروه های تنظیمی نسبت به شرایطی که تعداد گروه های تنظیمی محدود نباشد، به میزان 53/79% است. نتایج پیاده سازی روش پیشنهادی بر روی بخش توزیع شبکه 30 شین IEEE دلالت بر برتری قابل توجه 52/47 % در زمان عملکرد سیستم حفاظتی نسبت به طرح های حفاظت تطبیقی با منحنی مشخصه پیش فرض دارد.

    کلید واژگان: شبکه های توزیع فعال، حفاظت تطبیقی، حفاظت بهینه، پیکربندی های مختلف شبکه، رله های اضافه جریان جهت دار، انتخاب بهینه منحنی مشخصه استاندارد، محدودیت گروه های تنظیم مختلف رله ها، الگوریتم ژنتیک، DIgSILENT
    Mohammad Shamsi, Hamed Hashemi-Dezaki*

    The changes in configurations of distribution networks due to the outages of any upstream substations or distributed generations (DGs) is one of the essential challenges in the design of distribution systems. The changes in topologies of the network affect the protective schemes and might lead to coordination constraint violations in different operation modes and configurations. It is inevitable to appear some coordination constraint violation if only the base grid-connected operation mode and configuration is considered in the optimal protection settings. The adaptive protective schemes have several advantages compared to those using only one setting group, and their speed would be more desired. Although different research works have been done in the literature in adaptive protective schemes, there is a research gap about considering the limited number of setting groups for directional overcurrent relays (DOCRs), besides other aspects of active distribution networks and adaptive schemes. This research tries to fill such a research gap by proposing a new optimized adaptive protection system, considering various network configurations, by the limited number of setting groups. Since the proposed optimal settings for DOCRs are applied to a limited number of setting groups, it would be practical. Optimizing the standard relay characteristics in the proposed method is another contribution. Test results of applying the introduced method on the distribution portion of the IEEE 30-bus test system highlight the advantages of this study. Simulation results infer that 54.27% improvement in operating time of the protection system is achievable through applying the proposed method compared to available adaptive ones because of optimizing the relay characteristics.

    Keywords: Active distribution networks (ADNs), Adaptive protection, Different operation modes, Different network configurations, Directional overcurrent relays (DOCRs), Optimal selection of standard, relay characteristics, Setting groups, Genetic algorithm (GA), DI
  • S. Saffar *
    Acoustic scaling is one of the efficient techniques for measuring acousticparameters of large and luxury places. Due to the range of acousticscaling frequency, an airborne ultrasonic transducer has been used fortransporting the power to the air. One major problem of ultrasonictransducers, radiating acoustic energy into the air, is to find the properacoustic impedance of one or more matching layers. This work aims atdeveloping an original solution to the acoustic impedance mismatchbetween transducer and air. Therefore we consider three piezoelectrics,PZT, PVDF, and EMFi transducers and air that have high acousticimpedance deference. We proposed the use of a genetic algorithm (GA) toselect the best acoustic impedances for matching layers from the materialdatabase for a narrow band ultrasonic transducer that works at afrequency below the 1MHz by considering attenuation. This yields ahighly more efficient transmission coefficient. Also, the results showedthat by using the increasing number of layers we can increase our chanceto find the best sets of materials with valuable both in acoustic impedanceand low attenuation. Precisely, the transmission coefficient is almosthigher than 80% for the more studied cases.
    Keywords: acoustic impedance, matching layers, Ultrasonic transducers, Genetic algorithm (GA), PZT, PVDF, EMFi
  • Farhad Hosseinlou*, Najmeh Karami, Ehsan Dehghani Firoozabadi, Ehsan Jeddi

    The jacket structure is the key facility for the exploitation of marine resources. Offshore oil platforms located in an earthquake zone need to be analyzed for the structural response. A real offshore structure is always intricate and has to be idealized to diverse degree to fit in to the framework of the mathematical model for dynamic analysis. This work addresses the need for such a facilitated structural computation model. The planned scheme is based on laboratory work for improving the facilitated model. This study describes the scheme in employing the MDC associated with the GA method to create and update the facilitated structural model for analyzing the responses of a jacket platform. The facilitated modelling is first calculated based on MDC method, and then the platform model is refined and improved based on recorded modal features. Considering the presented model, the expense of analysis of jacket offshore structures is considerably reduced without incurring any loss of precision. Therefore, improvement of such approaches would be acutely beneficial to spread out technologies that can be applied for jacket structures with saving of both time and cost.

    Keywords: Jacket offshore platform, Vibration experiment, Mixed-dimensional coupling (MDC), Model updating, Genetic Algorithm (GA)
  • عمار کاریزی، سید محمد رضوی*، مهران تقی پور گرجی کلایی

    راه رفتن یکی از انواع ویژگی های بیومتریک است که به وسیله آن می توان هویت فرد را از ویدیوهای حاوی این بیومتریک شناسایی کرد. برای تشخیص هویت از طریق راه رفتن دو چالش جدی وجود دارد: 1) تغییر در جهت و زاویه راه رفتن 2) تغییر در ظاهر سوژه که به دلایل مختلف ازجمله حمل کیف یا تغییر پوشش ایجاد می شود. هر دو چالش ذکرشده عملکرد شناسایی هویت را به شدت تحت تاثیر قرار می دهد. در این مقاله روشی ارایه شده است که با هر دو چالش مذکور مقابله می کند. در روش پیشنهادی ابتدا جهت راه رفتن با استفاده از موقعیت تعدادی از پیکسل های منطقه پای انرژی تصویر راه رفتن (GEI) شناسایی می شود. این پیکسل ها به گونه ای انتخاب می شوند که بیشترین درصد شناسایی را به دنبال داشته باشند. و در ادامه با استفاده از الگوریتم ژنتیک قسمت هایی از GEI که در دو حالت حمل کیف و تغییر پوشش بیشترین تغییرات را دارند شناسایی و پوشانده می شوند. الگوریتم ژنتیک این قابلیت را دارد که ناحیه بهینه را با دقت خوبی شناسایی و حذف کند به طوریکه عملکرد سیستم در برابر تغییرات ظاهری مقاوم باشد. این سیستم منطقا باید عملکرد خوبی داشته باشد زیرا مرحله شناسایی جهت راه رفتن طوری طراحی شده که از نظر محاسباتی ارزان است و از طرفی الگوریتم ژنتیک به گونه ای عمل می کند که اطلاعات مفید بیشتری در مقایسه با سایر روش ها حفظ می کند. نتایج نشان می دهد که روش پیشنهادی به طور میانگین 9/95 درصد شناسایی دارد که این درصد شناسایی نشان از برتری عملکرد این روش نسبت به سایر روش هاست.

    کلید واژگان: الگوریتم ژنتیک، انرژی تصویر راه رفتن (GEI)، بیومتریک، تحلیل مولفه های اصلی (PCA)
    Ammar Karizi, Mehran Taghipour-Gorjikolaie

    Gait is a biometric feature that can be used to identify individuals from videos. Two main challenges are a change in the direction and angle of walking and a change in the appearance due to various reasons such as carrying a bag or any change in clothes that significantly affect identification. In the present paper, a method is proposed which addresses both challenges. In the proposed method, first, the direction of walking is determined using the position of several pixels in the foot zone of the gait energy image (GEI). The pixels are selected to have the maximum identification percentage. Then, the genetic algorithm is used to identify and mask zones of GEI with the most changes in both carrying a bag and changing clothes. The GA is capable of identifying and removing the optimized zone with a good precision that makes the system robust when there are changes in the appearance. Logically, the system should have a good performance because the walking direction identification stage is designed in an affordable computing time. Moreover, GA maintains more useful data comparing to similar techniques. According to the results, an average identification percentage of 95.9% was achieved which confirms the superiority of the proposed method over similar ones.

    Keywords: Biometric, Gait Energy Image (GEI), Genetic Algorithm (GA), Principal Component Analysis (PCA)
  • Ali Sanagooy Aghdam, Mohammad Ali Afshar Kazemi *, Abbas Toloie Eshlaghy

    Optimized asset tracking with Radio Frequency Identification (RFID) as a complicated innovation that requires much money to be implemented has become more popular in the healthcare industry. Considering the use of more antennas in each reader, we present a modern heuristic methods, hybrid of Genetic Algorithms (GA) and Simulated Annealing (SA) for the purpose of placing readers in an emergency department of a hospital with an RFID network. In this study, a multi-objective function is developed for the network coverage maximization and the minimization of total cost, tag reader collision, interference, energy consumption, and path loss in a simultaneous way. The proposed algorithm provides savings (on average) in the total cost of the RFID network through the efficient use of three types of readers with one, two and four antenna ports. Additionally, by testing three scenarios, the effect of algorithms in achieving the optimal solution is indicated by the simulated results.  Besides, the results of GA-SA is compared to the results of GA and other existing models in the relevant literature. It is shown that its main advantage is the use of multi-antenna RFID readers, which reduces the total cost of the RFID network and also increase network coverage with fewer readers and antennas. In other words, contributions for the research are proposing a hybrid GA-SA algorithm, developing a multi-objective function, testing the algorithm in a hospital setting, and comparing the results of GA-SA with GA.

    Keywords: Genetic algorithm (GA), Simulated annealing (SA), Asset tracking, Multi-antenna readers, Hospital
  • Dana Samadi, Hosein Taghaddos *, MohammadHosein Nili, Mojtaba Noghabaei

    Bridges play a critical role in the transportation system network; accordingly, assuring satisfaction with the service level of these structures is vital for bridge maintenance managers. Thus, it is vital to determine the optimum bridge maintenance plan (i.e., the optimum timing and type of repair activities applied to the bridge elements) considering the budget limitations. To optimize the bridge maintenance plan, some researchers have focused on developing optimization models, including the Genetic Algorithm (GA). However, a few studies have employed Bridge Information Modeling (BrIM) to enhance bridge maintenance management. This study focuses on developing an integrated framework based on BrIM and bridge maintenance optimization to utilize visualization capabilities of BrIM to assist maintenance managers in making decisions. The presented framework optimizes the bridge maintenance plan at the sub-element level. The BrIM automatically feeds into the developed GA optimization system. The introduced framework is successfully verified using a real-world case study.

    Keywords: Bridge Information Modeling (BrIM), Bridge Maintenance Plan, Genetic Algorithm (GA), Maintenance Optimization
  • Mehdi Bigdeli *, Jafar Aghajanloo, Davood Azizian
    Transformers are one of the most valuable assets of power systems. Maintenance and condition assessment of transformers has become one of the concerns of researchers due to a huge number of transformers has been approached to the end of their lifetimes. Transformer’s lifetime depends on the life of its insulation and the insulation’s life is strongly influenced by its moisture attraction as well. Thus, regarding the importance of moisture analysis, in this paper, a new method is introduced for moisture content determination in the transformer insulation system. The introduced method uses the dielectric response analysis in the frequency domain based on heuristic algorithms such as genetic algorithm and particle swarm optimization. First, the master curve of the dielectric response is modeled. Afterward, using the proposed method the master curve and the measured dielectric response curves are compared. By analyzing the comparison results, the moisture content of the paper insulation, the electrical conductivity of the insulating oil, and the dielectric model dimensions are determined. Finally, the proposed methods are applied to several practical samples and their capabilities are compared to the well-known conventional method.
    Keywords: Transformer Insulation, Moisture, Dielectric Frequency Response (DFR) Analysis, Genetic Algorithm (GA), Particle swarm optimization (PSO)
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