imperialist competitive algorithm
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ادغام مزارع بادی در مقیاسی عظیم با شبکه سراسری، چالش های متعددی را برای بهره بردارن سیستم قدرت به همراه دارد. یکی از مهم ترین موارد، استخراج حداکثر توان ممکن در شرایط کاری مختلف از ظرفیت نیروگاهی نصب شده است. یکی از راهکارهای مرسوم در نیروگاه های بادی مجهز به ژنراتورهای القایی از دو سو تغذیه، کنترل توان خروجی روتور به کمک بهره گیری از کنترل کننده تناسبی-انتگرالی است. در این مقاله از الگوریتم بهینه سازی رقابت استعماری اصلاح شده با نظریه آشوب برای تنظیم ضرایب کنترل کننده 20 توربین بادی 2 مگاواتی قرارگرفته در یک ریزشبکه استفاده شده است. نتایج بدست آمده نشان می دهد که استفاده از نظریه اشوب، موجب بهبود کیفیت همگرایی ردگیری نقاط حداکثر توان در هنگام تغییرات سریع سرعت باد می گردد. در استراتژی ردیابی نقطه حداکثر توان با کنترل کننده تناسبی-انتگرالی کلاسیک، زمانی که سرعت باد از مقداری به مقداری دیگر تغییر می یابد، گشتاور ایجاد شده حاوی ریپل زیادی بوده و این کنترل کننده در غلبه بر خاصیت غیرخطی و ریپل گشتاور ناتوان است. این ضعف باعث افزایش استرس وارد شده به سیستم شده و می تواند باعث آسیب دیدن تجهیزات ساختاری ژنراتور گردد. در عین حال،نتایج بدست آمده با استفاده از الگوریتم رقابت استعماری مبتنی بر آشوب، نشان می دهد که نه تنها محتوای ریپل به طور قابل ملاحظه ای کاهش یافته بلکه بیش از 40٪ از مقدار زیرجهش نیز کاسته می شود.
کلید واژگان: ردیابی نقطه حداکثر توان، الگوریتم رقابت استعماری حل کننده مبتنی بر آشوب، توربین بادیMaximum power point tracking of wind turbines with a chaotic-based imperialist competition algorithmIntegrating large-scale wind farms with the main grid poses several challenges to operating the power utilities. One of the most important is to extract the maximum possible power in different operating conditions from the installed capacity. One of the common solutions in wind power plants equipped with doubly-feed induction generators is to control the output power of the rotor by using a proportional-integral controller.In this paper, a modified chaos-based imperialist competition algorithm is used to manage the control coefficients of twenty wind turbines (2 MW) located in a microgrid. The results show that the use of chaos theory improves the quality of convergence tracking of maximum power points during rapid changes in wind speed. In the classical proportional-integral controller, when the wind speed changes rapidly, the generated torque contains a large ripple and this controller is unable to overcome the nonlinear and ripple torque properties. This weakness increases the stress on the system and can damage the structural equipment of the generator. At the same time, the results obtained using the chaotic-based imperialist competition algorithm show that not only the ripple content is significantly reduced, but also more than 40% of its subsurface value is reduced.
Keywords: Maximum power point tracking, Imperialist competitive algorithm, Chaos-based, Wind Turbine -
محاسبات مه برای حل چالش های متعدد محیط محاسبات ابری مانند زمان تاخیر بالا، ظرفیت کم و نقص شبکه ارایه گردیده است. در محیط محاسبات مه، دستگاه های اینترنت اشیاء بعنوان یک کاشین محاسباتی کوچک با قابلیت پردازش و ارسال و دریافت اطلاعات، زیر ساخت یک مه را تشکیل می دهند. در محیط مه، پردازش کارها و وظایف و ذخیره داده های اینترنت اشیاء بجای ارسال برای سرورهای دور در مراکز داده ابری به صورت محلی در دستگاه های اینترنت اشیاء صورت می پذیرد که این قابلیت منجر به ارایه پاسخ سریع تر و با تاخیر کمتر و افزایش کیفیت ارایه خدمات در محیط مه می گردد. بنابراین می توان گفت که محاسبات مه بهترین انتخاب برای فعال کردن اینترنت اشیاء در راستای ارایه خدمات کارآمد و امن برای بسیاری از کاربران در لبه شبکه محسوب می شود. در محاسبات مه، مدیریت منابع و زمانبندی کار با در نظر گرفتن محدودیت های انرژی، زمان، تاخیر چالش بزرگی محسوب می شود. در این مقاله راهکار زمانبندی وظایف مبتنی بر الگوریتم رقابت استعماری برای محاسبات مه ارایه شده است. در راهکار پیشنهادی جمعیت اولیه بطور تصادفی شامل وظایف و ماشین ها شکل می گیرد و تابع ارزیابی بر اساس معیارهای انرژی، زمان و هزینه تعریف شده و با به کارگیری دو عملگر جذب و انقلاب، الگوریتم زمانبندی وظایف مبتنی بر رقابت استعماری (ICTF) ارایه می گردد. نتایج شبیه سازی نشان می دهد که ICTF در معیارهای Makespan ، بهره وری منابع، مصرف انرژی و انرژی باقیمانده نسبت به سایر روش های مشابه کارایی بالاتری دارد.
کلید واژگان: محاسبات مه، مدیریت منابع، زمانبندی وظایف، الگوریتم رقابت استعماریFog computing address numerous cloud computing challenges such as high latency, low capacity and network failure. The cloud computing infrastructure includes a large number of IoT devices with the ability to process in the cloud environment. In fog computing, processing and storage provide on IoT devices locally instead of remote servers, therefore, fog computing is the best choice to enable IoT in order to provide efficient, faster and secure services for many users on the edge of the network. Fog computing have a variety of challenges. One of these important challenges is resource management and task scheduling such that solving this problem has a great impact on system efficiency and service quality. In this paper, we present a task scheduling approach based on the imperialist competition algorithm namely, Imperialist Competitive Algorithm-based Task Scheduling in Fog Computing (ICTF). In the proposed method, we consider the search space as a directional graph. Assume that each task that contains a set of tasks is a graph with a root node and an end leaf node whose middle nodes are the task set. Each path in this graph that starts at the root node and ends at the leaf is a solution represented by a string. This solution is modeled as a country. Therefore, in the proposed method, the concept of country includes the tasks of a job along with the fog nodes that are assigned to these tasks. The initial population consists of a random number of these solutions. ICTF presents a cost function consisting of three important criteria for assessing the initial population of countries and determining the imperialists and colonies countries include energy, execution time and execution cost. The assimilation operation is performed on two different members of countries, namely the imperialists and colonies country, and two new types of members are created called children. The countries participating in this process are among the best countries and are selected using the cost function. In this process, the best offspring produced are passed on to the next generation, and this operation continues until the final population of the countries is obtained. The assimilation operator has different models and in this article we use the two-point assimilation operator. The name of the revolution operator used in this algorithm is the inverse of the task. This operator randomly selects two tasks belonging to a fog node and moves them together. The above operation is repeated until the population converges and reaches the final answer. We show that our proposed approach is more efficient in terms of makespan, resource utilization, energy consumption and remaining energy compared to the similar approach.
Keywords: Fog computing, resource management, task scheduling, imperialist competitive algorithm -
Scientia Iranica, Volume:29 Issue: 6, Nov-Dec 2022, PP 2995 -3015This paper presents a new hybrid algorithm generated by combining advantageous features of the Imperialist Competitive Algorithm (ICA) and Biogeography Based Optimization (BBO) to create an effective search technique. Although the ICA performs fairly well in the exploration phase, it is less effective in the exploitation stage. In addition, its convergence speed is problematic in some instances. Meanwhile, the BBO method's migration operator strongly emphasizes local search to focus on promising solutions and finds the optimum solution more precisely. The combination of these two algorithms leads to a robust hybrid algorithm that has both exploratory and exploitative functionalities. The proposed hybrid algorithm is named Migration-Based Imperialist Competitive Algorithm (MBICA). To validate its performance, MBICA is used to optimize a variety of benchmark truss structures. Compared to some other methods, this algorithm converges to better or at least identical solutions by reducing the number of structural analyses. Finally, the results of the standard BBO, ICA, and other recently developed metaheuristic optimization methods are compared with the results of this study.Keywords: Hybrid algorithm, Imperialist competitive algorithm, Biogeography-based optimization, meta-heuristic algorithms, Optimum design, Truss structures design, Structural optimization
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پیش بینی قیمت سهام در بورس اوراق بهادار از جمله چالش برانگیزترین مباحث در مقوله پیش بینی است که توجهات بسیاری از جمله محققان را به خود جلب کرده است. عوامل مختلف درگیر در بورس اوراق بهادار سبب شده است تا بازار بورس همیشه از خود فرآیندی پویا و پیچیده داشته باشند. لذا پژوهش گران بر آن شده اند تا در پیش بینی رفتار بورس، به دنبال روش های نوینی باشند که دربرابر عدم ایستایی و پیچیده بودن مقاوم باشند. در این پژوهش یک مدل ترکیبی دوگانه متشکل از دو سامانه استنتاج فازی و یک الگوریتم رقابت استعماری به صورت ترکیبی استفاده شده است که یک سامانه فازی برای ایجاد مدلی برای پیش بینی قیمت سهام براساس 10 متغیر تاثیرگذار بر قیمت سهام استفاده می شود که قوانین فازی موتور استنتاج این سامانه فازی توسط نسخه بهبود یافته فازی جدید الگوریتم رقابت استعماری به دست می آید و پارامترهای الگوریتم رقابت استعماری نیز توسط یک سامانه فازی دیگر به نام تنظیم کننده پارامترها ، تعیین می شوند. به منظور ارزیابی عملکرد مدل پیشنهادی اطلاعات مرتبط با قیمت سهام شش شرکت فعال در بورس اوراق بهادار تهران در نظر گرفته شده و هشت مدل پیش بینی قیمت سهام در دو گروه الگوریتم به همراه مدل پیشنهادی پیاده سازی شدند. نتایج به دست آمده نشان از عملکرد بهتر مدل پیشنهادی از جهت کیفیت نتایج پیش بینی شده و انحراف کم نتایج فاز آزمون از فاز آموزش دارد.
کلید واژگان: پیش بینی قیمت سهام، سامانه استنتاج فازی ممدانی، شبک عصبی، درخت تصمیم، جنگل تصادفی، ماشین بردار پشتیبان، الگوریتم رقابت استعماریInvesting on the stock exchange, as one of the financial resources, has always been a favorite among many investors. Today, one of the areas, where the prediction is its particular importance issue, is financial area, especially stock exchanges. The main objective of the markets is the future trend prices prediction in order to adopt a suitable strategy for buying or selling. In general, an investor should be predicted the future status of the time, the amount and location of his assets in a way that increases the return on his assets. Stock price prediction is one of the most challenging topics in the field of forecasting, which has attracted many attentions from researchers. The various factors of the markets have caused the situation that they always have a dynamic and complex process. Therefore, researchers have been determined to look for new prediction methods of stock price, which will reduce the instability and complexity of the markets. In fact, the most of recent studies have shown that the stock market is a nonlinear, dynamic, and non-parametric system that is affected by various economic factors. The applications of artificial intelligence and machine learning techniques to identify the relationship between the factors and stock price exchanges can be organized in seven major groups such as neural networks and deep learning, support vector machine, decision tree and random forest, k nearest neighbor, regression, Bayesian networks and fuzzy inference-base methods. Due to the mentioned prediction methods have their own challenges, hydridizations of the meta-heuristic algorithms and the methods were applied to stock price prediction. In this paper, a new hybridization of Fuzzy Inference System and a novel modified Fuzzy Imperialist Competitive Algorithm (FICA+FIS) are proposed to stock price prediction. To achieve this aim, two Fuzzy Inference Systems are designed to tuing the ICA’s parameters based on three effective factors in search strategy and to predict stock price based on 10 effective economic factors. The candidate fuzzy rules set of the inference engine is obtained by the FICA for the second FIS and six fuzzy rules of the first FIS are designed based on the ICA’s behaviour. The FICA+FIS has 10 inputs of the stock price variables including the lowest stock price, the highest stock price, the initial stock price, the trading volume, the trading value, the first market index of the trading floor, the total market price index, the dollar exchange rate, the global price per ounce of gold, the global oil price, and its output is also the stock price. The inputs and output variables consist of three linguistic vairables such as Low, Medium, and High with triangular membership functions. Each country (search agent) of the FICA contains information on all the fuzzy rules of the inference engine attributed to the country and has r×12 elements, where r is the number of fuzzy rules. The FICA’s objective function is the mean square error (MSE) to evaluate the power of each country. A challenge of the ICA is the proper tuning paprameters such as the Revolution Probability (Prevolve), Assimilation Coefficient (Beta) and the Colonies Mean Cost Coefficient (zeta), which has a great impact on the efficiency of the algorithm (precision and time of access to solution). These parameters are usually constant and according to different problems, they have different values and are given experimentally. In this paper, the parameters are tuned based on the number of iterations that the best objective function value has not improved (UN), the number of imperialist (Ni) and the current number iteration (Iter). To this aim, a FIS is designed based on six fuzzy rules that UN, Ni and Iter are its input variables and Prevolve, Beta and zeta are its output variables. To analyze the efficiency of the FICA+FIS as a case study, six datasets are collocted from six companies which were active between 1389 to 1394 in Tehran Stock Exchange such as Pars Oil, Iran Khodro, Motogen, Ghadir, Tidewater and Mobarakeh. The information of around 2000 days are collected for each company and the data are divided to train and test data based on cross validation 10-fold. To compare the performance of the FICA+FIS, two groups of stock price prediction methods were implemented. In the first group, the fuzzy rules of the FIS’s engine to stock price prediction are obtained by the classic draft of the Imperialist Competitive Algorithm (ICA+FIS), the Genetic Algorithm (GA+FIS) and the Whale Optimization Algorithm (WOA+FIS), which are used to compare with the FICA. The second group includes classic stock price prediction methods such as multi-layered neural network (NN), support vector machine (SVM), CART decision tree (DT-CART), random forest (RF) and Gaussian process regression (GPR), which are used to compare with the FICA+FIS. The experimental results show that first, the improved fuzzy draft of the ICA performed better than its classic draft, the GA and the WOA, and second, the performance of the FICA FIS is better than other investigated algorithms in both training and testing phases, although the DT is a competitor in the training phase and the RF is a competitor in the test phase on some datasets.
Keywords: Stock Price Prediction, Fuzzy Inference Systems, Neural Networks, Decision Tree, Random Forest, Support Vector Machine, Imperialist Competitive Algorithm -
کنترل فرکانس بار (LFC) یکی از مهمترین موضوعات در سیستم های قدرت الکتریکی می باشد. در صنعت معمولا از کنترل کننده های تناسبی انتگرالی (PI) برای این امر استفاده می شود. در این مقاله از کنترل کننده Fuzzy-PID با توابع عضویت بهینه شده برای کنترل فرکانس بار در یک سیستم دو ناحیه ای استفاده شده است. به منظور تعیین محل توابع عضویت ورودی ها و نیز بهره های کنترل کننده های Fuzzy-PID از بهینه سازی استفاده شده است. در این مطالعه الگوریتم رقابت استعماری (ICA) جهت بهینه سازی بکار گرفته شده است. شبیه سازی ها در محیط سیمولینک برنامه MATLAB و در حضور منابع انرژی بادی و بارهای متغیر انجام پذیرفته است. به منظور مقایسه، شبیه سازی ها با کنترل کننده FOPID بهینه شده با الگوریتم رقابت استعماری نیز انجام شده است. همچنین به منظور بررسی میزان مقاوم بودن کنترل کننده پیشنهادی در برابر عدم قطعیت های موجود در سیستم، شبیه سازی ها با تغییرات پارامترهای سیستم دو ناحیه ای نیز صورت گرفته است. نتایج شبیه سازی ها، عملکرد مطلوب کنترل کننده پیشنهادی را نشان می دهند.کلید واژگان: کنترل فرکانس بار، الگوریتم رقابت استعماری، توابع عضویت بهینه شده، کنترل کننده fuzzy-PID، کنترل کنندهFOPIDLoad Frequency Control (LFC) is one the most important topics in power systems. To this end, Proportional-integral (PI) controllers is usually employed in industry. In this paper, a Fuzzy-PID controller with optimized membership functions has been designed for LFC in a two-area power system. Optimization has been employed in order to define the location of input membership functions and gains of Fuzzy-PID controllers. Imperialist Competitive Algorithm (ICA) has been used for optimization in this study. Simulations have been carried out in MATLAM/SIMULINK in presence of wind power and variable loads. In order to conduct comparisons, the simulations have been carried out using the fractional order PID (FOPID) controller optimized by ICA. Moreover, to verify robustness degree of the proposed controller against system uncertainties, simulations have been done by changing the parameters of two-area system. Results of simulations illustrate good performance of the proposed controller.Keywords: Load Frequency Control, Fuzzy-PID controller, Imperialist Competitive Algorithm, Optimized membership functions, FOPID controller
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To achieve high-quality software, different tasks such as testing should be performed. Testing is known as a complex and time-consuming task. Efficient test suite generation (TSG) methods are required to suggest the best data for test designers to obtain better coverage in terms of testing criteria. In recent years, researchers to generate test data in time-efficient ways have presented different types of methods. Evolutionary and swarm-based methods are among them. This work is aimed to study the applicability of swarm-based methods for efficient test data generation in EvoSuite. The Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) are used here. These methods are added to the EvoSuite. The methods are adapted to work in a discrete search space of test data generation problem. Also, a movement pattern is presented for generating new solutions. The performances of the presented methods are compared over 103 java classes with two built-in genetic-based methods in EvoSuite. The results show that swarm-based methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.Keywords: Test data generation, Firefly Algorithm, particle swarm optimization, Teaching Learning Based Optimization, Imperialist Competitive Algorithm, EvoSuite
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هدف از پژوهش حاضر، پیش بینی تقاضای کل مصرف برق کشور ایران بر پایه شاخص های اقتصادی- اجتماعی و با استفاده از روش های فرا ابتکاری است. برای رسیدن به این هدف دو استراتژی مختلف مورد بررسی قرار گرفته است. در استراتژی اول از الگوریتم ژنتیک، الگوریتم بهینه سازی انبوه ذرات و الگوریتم رقابت استعماری برای تعیین معادلات پیش بینی تقاضای انرژی الکتریکی استفاده شده است. بدین منظور اطلاعات مربوط به شاخص های جمعیت، تولید ناخالص داخلی، قیمت برق و مصرف برق طی سال های 1347 تا 1394 مورد استفاده قرارگرفته و مدل های پیش بینی تقاضا به دو صورت خطی و غیرخطی ارایه شده است. در استراتژی دوم از شبکه های عصبی مصنوعی آموزش داده شده با الگوریتم های فراابتکاری فوق الذکر برای پیش بینی تقاضای برق بر پایه همان متغیرهای ورودی تعیین شده در استراتژی اول استفاده شده است. نتایح نشان داد مدل نمایی توسعه یافته با الگوریتم بهینه سازی انبوه ذرات، با درصد قدرمطلق میانگین خطای 85/2%، بهترین دقت را در پیش بینی تقاضای انرژی الکتریکی ایران دارد. تقاضای برق ایران تا سال 1404 پیش بینی شد و انتظار می رود به مقدار 324 تراوات ساعت برسد.
کلید واژگان: پیش بینی تقاضای برق، شبکه عصبی مصنوعی، الگوریتم ژنتیک، الگوریتم بهینه سازی انبوه ذرات، الگوریتم رقابت استعماریThis study aims to forecast Iran's electricity demand by using meta-heuristic algorithms, and based on economic and social indexes. To approach the goal, two strategies are considered. In the first strategy, genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA) are used to determine equations of electricity demand based on economic and social indexes consisted of population, gross domestic product (GDP), electricity price, and electricity consumption during the years 1968 to 2015. In this regard, linear and nonlinear models are developed. In the second strategy, artificial neural networks (ANNs) trained by meta-heuristic algorithms (GA, PSO, and ICA) are used to forecast electricity demand. The results show that nonlinear PSO with %2.85 mean absolute percentage error (MAPE) is a suitable model to forecast Iran's electrical energy demand. Iran's electricity demand would reach 324 terawatt-hours (TWh) up to the year 2025.
Keywords: Electricity demand forecasting, Artificial neural networks, Genetic algorithm, Particle swarm optimization, Imperialist competitive algorithm -
مصرف انرژی در شبکه های حسگر بیسیم که از تعدادی زیادی حسگر کوچک که با منبع تغذیه محدود تشکیل شده است، اهمیت بسیار زیادی دارد. این حسگرها با هدف انجام وظایف خاص در محیط های مختلف توزیع می شوند و با اتمام انرژی گره ها، ممکن است اطلاعات بخشی از شبکه از دسترس خارج و کارآیی شبکه به خطر بیافتد. اخیرا جهت افزایش طول عمر این شبکه ها، از گره های برداشت که قابلیت شارژ با انرژی های تجدیدپذیر را دارند، استفاده شده است. به دلیل هزینه بالای گره های برداشت، کمینه کردن تعداد آنها با کمترین تاثیر روی عملکرد یکی از جنبه های مهم در طراحی این نوع از شبکه ها است. در این مقاله می خواهیم با افزودن حداقل تعداد گره خورشیدی و مدیریت مصرف انرژی طول عمر شبکه را افزایش دهیم. در این راستا، برای تعیین مکان گره های برداشت انرژی، الگوریتم تکاملی رقابت استعماری مورد استفاده قرار می گیرد. الگوریتم رقابت استعماری تعداد گره های برداشت انرژی و مختصات تخمینی آنها را تعیین می کند. بعد از مشخص شدن تعداد گره های برداشت انرژی، با استفاده از روش K-Means مختصات دقیق گره های برداشت و خوشه ها بدست می آید. نتایج شبیه سازی نشان می دهد که استفاده از روش پیشنهادی با مدیریت انرژی در شبکه حسگر بیسیم، می تواند طول عمر و در نتیجه کارایی آن را تا حد قابل قبولی افزایش دهد.کلید واژگان: شبکه حسگر بیسیم، گره های برداشت خورشیدی، الگوریتم رقابت استعماری، انرژی، k-meanEnergy consumption is a significant factor in wireless sensor networks (WSNs) which are consists of so many tiny and battery-based sensors. These sensors are distributed in different environments to perform distinct duties and whenever they consume the whole energy of their batteries, some parts of the necessary data of the network or its efficiency can be missed. Recently, in order to enhance these networks, extra energy extraction nodes which can harvest the energy from the environment, are used between sensors and the base station. Due to the high cost of energy harvesting nodes, minimizing the number of these nodes without affecting the quality of the signals is an important aspect of the wireless sensor network design. Therefore, the challenge is determining the efficient number of energy harvest nodes and their coordinates. In this work, for the first time, the Imperialist Competitive Algorithm (ICA) as one of the most advanced evolutionary algorithms is used to determine the minimum number of the energy harvest node. Based on the ICA’s results, the K-Means algorithm is applied to clustering nodes. The simulation results show that the suggested method can improve the performance of the WSN by increasing its lifetime.Keywords: Wireless Sensor Network, Energy Harvesting nodes, Imperialist Competitive Algorithm, Energy, k-mean
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The present study investigated economic load dispatch problem, taking into consideration valve point loading effect using an imperialist competitive algorithm. To portray the efficiency of the proposed method, the algorithm was applied to two popular test systems in the area including 13 and 40 thermal units. The obtained results from the algorithm were compared to those of other algorithms which indicated the efficiency and response of the proposed algorithm in solving economic load dispatch problem.
Keywords: Economic Load Dispatch, Optimization Algorithm, Imperialist Competitive Algorithm, Valve PointEffect -
Developments in power systems like the installation of new generation resources and interconnections may increase short-circuit current levels and consequently, impose additional costs of replacing circuit breakers and equipment. In these cases, one of the best methods to reduce short-circuit currents in power systems and avoid significant replacement costs is to use fault current limiters (FCLs). This paper suggests a new method for optimally locating and sizing FCLs using an imperialist competitive algorithm (ICA). The ICA finds the optimal locations and sizes of FCLs such that not only are short-circuit currents reduced, but the size of the installed FCLs is also minimized, and the system reliability is increased. Indeed, three indices including the short-circuit level, the economic cost of FCLs, and the lost power are integrated into an objective function with a new formulation. The results obtained from multiple executions of the suggested procedure for the 39-bus New England benchmark system confirm that the formulation of indices in the objective function is suitable and the indices can be prioritized easily. Also, the results indicate that the ICA can find the optimal locations and sizes of FCLs with good convergence and accuracy considering the specified objectives and priorities.Keywords: power system, imperialist competitive algorithm, fault current limiter, reliability
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In traditional scheduling problems and in many real-world applications the production operations are scheduled regardless of distribution decisions. Indeed, the completion time of a job in such problems is traditionally defined as the time when the production sequences of a job are finished. However, in many practical environments completed orders are delivered to customers immediately after production stages without any further inventory storage. Therefore, in this paper, we investigate an integrated scheduling model of production and distribution problems simultaneously. It is assumed that products are proceed through a permutation flow shop scheduling manufacturing system and delivered to customers via available vehicles. The objective in our integrated model is to minimize maximum returning time (MRT), which is the time that last vehicle delivers last order to relevant customer and returns to production center. The problem formulated mathematically, and then an improved imperialist competitive algorithm (I-ICA) is proposed for solving it. Furthermore, sufficient numbers of test problems are generated for computational study. Various parameters of the algorithm are analyzed to calibrate the algorithm by means of the Taguchi method. At the end, the effectiveness of the proposed model and suggested algorithm is evaluated through a computational study where obtained results show the appropriate performance of integrated model and solving approach with regard to the other algorithms.Keywords: Integrated modeling approach, Flow shop scheduling, distribution with routing, Imperialist competitive algorithm
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Journal of Artificial Intelligence and Data Mining, Volume:6 Issue: 2, Summer-Autumn 2018, PP 297 -311Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters which may lead to reducing efficiency and performance. In fact, in WSNs topology, increasing a layer of cluster is a tradeoff between time complexity and energy efficiency. In this study, regarding the most important WSNs design parameters, a novel dynamic multilayer hierarchy clustering approach using evolutionary algorithms for densely deployed WSN is proposed. Different evolutionary algorithms such as Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to find an efficient evolutionary algorithm for implementation of the clustering proposed method. The obtained results demonstrate the PSO performance is more efficient compared with other algorithms to provide max network coverage, efficient cluster formation and network traffic reduction. The simulation results of multilayer WSN clustering design through PSO algorithm show that this novel approach reduces the energy communication significantly and increases lifetime of network up to 2.29 times with providing full network coverage (100%) till 350 rounds (56% of network lifetime) compared with WEEC and LEACH-ICA clsutering.Keywords: Wireless sensor networks, cluster head, Genetic Algorithm, Imperialist Competitive Algorithm, Network Lifetime
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Journal of Artificial Intelligence and Data Mining, Volume:6 Issue: 1, Winter-Spring 2018, PP 59 -67The human has always been to find the best in all things. This Perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables and find the best acceptable answer Due to the limitations of the problem, So that the objective function is minimum or maximum. One of the ways inaccurate optimization is meta-heuristics so that Inspired by nature, usually are looking for the optimal solution. in recent years, much effort has been done to improve or create metaheuristic algorithms. One of the ways to make improvements in meta-heuristic methods is using of combination. In this paper, a hybrid optimization algorithm based on imperialist competitive algorithm is presented. The used ideas are: assimilation operation with a variable parameter and the war function that is based on mathematical model of war in the real world. These changes led to increase the speed find the global optimum and reduce the search steps is in contrast with other metaheuristic. So that the evaluations done more than 80% of the test cases, in comparison to Imperialist Competitive Algorithm, Social Based Algorithm , Cuckoo Optimization Algorithm and Genetic Algorithm, the proposed algorithm was superior.Keywords: Optimization Method, Imperialist Competitive Algorithm, Meta-heuristic Algorithm, Hybrid Algorithm
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Scientia Iranica, Volume:24 Issue: 4, 2017, PP 2062 -2081Classi cation is an important machine learning technique used to predict group membership for data instances. In this paper, we propose an ecient prototypebased classi cation approach in the data classi cation literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classi er is
called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three di erent tness functions on twelve typical test datasets from the UCI Machine Learning Repository. The performance of the proposed EICA is compared with well-developed algorithms in classi cation including original Imperialist Competitive Algorithm (ICA), the Arti cial Bee Colony (ABC), the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), the Gravitational Search Algorithm (GSA), the Grouping Gravitational Search Algorithm (GGSA), and nine well-known classi cation techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classi cation techniques.Keywords: Prototype-based classification, Imperialist competitive algorithm, UCI machine learning repository -
Journal of Artificial Intelligence in Electrical Engineering, Volume:5 Issue: 19, Autumn 2016, PP 55 -59In this study, solutions for machinery layout with the aim of reducing transportation costs will be discussed. To do this, imperialist competitive algorithm (ICA), which is a very complicated and effective meta-heuristic algorithm is introduced. In the offered algorithm, different kinds of machinery layout are considered in single-row as country and then the best possible layout is developed based on ICA phases such as revolution and absorption policy, population displacement and production and applying its steps on the machinery, possible layouts were investigated by considering the distance of machinery pairs and components manufacturing routes.. The results show that using this algorithm, layout of machines would have the most favorable form based on used machinery to produce components.Keywords: Imperialist competitive algorithm, machinery layout, Transportation Cost
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Journal of Operation and Automation in Power Engineering, Volume:4 Issue: 2, Summer - Autumn 2016, PP 165 -174This paper presents a new and useful methodology for simultaneous allocation of sectionalizer switches and distributed energy resources (DERs) considering both reliability and supply security aspects. The proposed algorithm defines the proper locations of sectionalizer switching devices in radial distribution networks considering the effect of DER units in the presented cost function and other optimization constraints such as providing the maximum number of costumers to be supplied by DER units in islanded distribution systems after possible outages. In this paper, the main goal of cost function is to minimize the total cost of expected energy not supplied (EENS) with regard to impacts of load priority and optimum load shedding in the both grid connected and islanding states after possible outages. The proposed method is simulated and tested on a case study system in both cases of with DER and non DER situations. Also, this paper evaluates the number and amount of DER, switch and different DER penetration percentage effects in cost function value. For solving of mentioned problem, this paper uses a new and strong method based on imperialist competitive algorithm (ICA). Simulation and numerical results show the effectiveness of the proposed algorithm for placement of switch and DER units in the radial distribution network simultaneously.Keywords: Optimal switch placement, Optimal DER placement, Reliability assessment, Imperialist competitive algorithm, Supply security aspects
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بخش های مختلف شبکه قدرت تحت تاثیر عوامل مختلفی از جمله حوادث طبیعی و یا خطاهای پیش بینی نشده قرار دارند. این موضوع در زمینه تقویت شبکه-های موجود و یا طراحی شبکه های الکتریکی جهت تامین توان این مراکز دارای اهمیت است؛ بهطوریکه آمادگی سیستم برای بازیابی در کوتاهترین زمان ممکن و با کمترین خسارت افزایش یابد. هدف این مطالعه، افزایش امنیت تامین انرژی الکتریکی در مراکز حساس با استفاده از منابع انرژی تجدید پذیر است بهطوریکه قیود فنی شبکه در کنار عدم قطعیت منابع انرژی و اولویت قطع بارهای حساس برآورده شوند. با توجه به حضور منابع تولید پراکنده و عدم قطعیت تولید توان آنها، روش های متداول بررسی امنیت تامین انرژی به طور مستقیم قابل اعمال نیستند. مدلسازی نهایی بر اساس الگوریتم رقابت استعماری، کاهش سناریو و پخش بار بهینه انجام شده است و برای ارزیابی کفایت تامین انرژی مصرف کنندگان از مجموعه ای از مسئله های پخش بار بهینه استفاده شده است. مدل ارائه شده در محیط نرم افزارهای MATLAB و GAMS حل شده است و ترکیب بهینه منابع انرژی تجدید پذیر جهت جهت افزایش قابلیت اطمینان بدست آمده است. نتایج نهایی نشان دهنده کارایی الگوریتم پیشنهادی در طراحی مناسب منابع انرژی ترکیبی و افزایش قابل توجه امنیت تامین انرژی الکتریکی است.کلید واژگان: قابلیت اطمینان، پخش بار بهینه، انرژی های تجدید پذیر، انرژی تامین نشده مورد انتظار، الگوریتم رقابت استعماریDivisions of power system are affected by different parameters like natural disasters and unforeseen events. This case is important for improvement of present networks or design of new networks, and it can prepare the network for fast restoration with minimum damage. The objective of this study is to increase security of energy supply in sensitive points using renewable energy resources so that networks technical constraints is satisfied beside energy resources uncertainty and sensitive loads priorities. Due to the presence of DG units and the uncertainty of their power, common methods of security evaluation of energy supply are not applicable directly. Finally, a comprehensive algorithm based on imperialist competitive algorithm, scenario reduction, and optimal power flow is proposed and supply adequacy of consumers is evaluated by solving a set of optimal power flow equations. The proposed model is presented in MATLAB and GAMS environments and optimal mix of energy resources is obtained to increase reliability of network. Final results show the effectiveness of the proposed algorithm in optimal design of hybrid energy resources and remarkable improvement of energy supply security.Keywords: Reliability, Optimal Power Flow, Renewable Energy, xpected Energy Not Supplied, Imperialist Competitive Algorithm
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Journal of Artificial Intelligence in Electrical Engineering, Volume:4 Issue: 16, Winter 2016, PP 35 -44The growth in demand for electric power and the rapid increase in fuel costs, in whole of the world need to discover new energy resources for electricity production. Among of the nonconventional resources, wind and solar energy, is known as the most promising devices electricity production in the future. In this thesis, we study follows to long-term generation scheduling of power systems in the presence of wind units considering demand response programs for a power generation system with the conventional structure containing 10 units of thermal power plants, 2 units of large-scale wind farms, with regard to demand response programs. In this study, the model of equation order 2, used to model the cost of thermal power plants. Due to the apparent wind farm modeling of wind speed in the months of the year has been used the productivity of each month. For modeling the demand, response of theconcept of virtual generation demand resources is used and production costs of the final objective function are applied. All simulations, performed in MATLAB. The results showed that the cost of basic state allocated more values by itself, by considering responding load would be happened the cost reduction and the amount of this reduction changes with participation value and increases amount of lower cost by increasing participation value.Keywords: Planning production, wind farm, Imperialist Competitive Algorithm, responding load, request, resource
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Journal of Operation and Automation in Power Engineering, Volume:4 Issue: 1, Winter - Spring 2016, PP 29 -41Nowadays, demand response programs (DRPs) play an important role in price reduction and reliability improvement. In this paper, an optimal integrated model for the emergency demand response program (EDRP) and dynamic economic emission dispatch (DEED) problem has been developed. Customers behavior is modeled based on the price elasticity matrix (PEM) by which the level of DRP is determined for a given type of customer. Valve-point loading effect, prohibited operating zones (POZs), and the other non-linear constraints make the DEED problem into a nonconvex and non-smooth multi-objective optimization problem. In the proposed model, the fuel cost and emission are minimized and the optimal incentive is determined simultaneously. The imperialist competitive algorithm (ICA) has solved the combined problem. The proposed model is applied on a ten units test system and results indicate the practical benefits of the proposed model. Finally, depending on different policies, DRPs are prioritized by using strategy success indices.Keywords: Emergency demand response program, Dynamic economic emission dispatch, Imperialist competitive algorithm, Optimal incentive, Strategy success indices
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مجازی سازی و استفاده از ماشین های مجازی اساس تکنولوژی پردازش ابری است. ماشین های مجازی پس از مکان یابی، بر روی ماشین های فیزیکی منتخب اجرا می شوند. منظور از مکان یابی، انتخاب میزبان مناسب برای ماشین های مجازی موجود است. مکان یابی ماشین های مجازی در میزان مصرف انرژی و جلوگیری از هدر رفتن منابع در بسترهای سخت افزاری، نقش اساسی دارند. از طرفی، توسعه روزافزون سیستم های ابری فرآیند مکان یابی ماشین های مجازی را پیچیده تر ساخته است. در پژوهش حاضر با در نظر گرفتن دو هدف کاهش مصرف انرژی و کاهش اتلاف منابع، مکان یابی کارا ارائه شده است. در روش پیشنهادی، ضمن استفاده از روش متداول تبدیل مسئله مکان یابی به مسئله بهینه سازی، از الگوریتم نوظهور رقابت استعماری استفاده شده و با معرفی مفهومی جدید بنام نماینده، روند تولید جواب های جدید و بررسی فضای جستجو هدفمند شده است. نتایج شبیه سازی نشان می دهد، انتخاب الگوریتم رقابت استعماری در حل مسئله مکان یابی ماشین های مجازی، با معرفی و استفاده از مفهوم نماینده، همراه با موفقیت بوده و الگوریتم پیشنهادی در مقایسه با الگوریتم هایی چون GGAو FFD پاسخ های قابل قبولی را ارائه می کند.کلید واژگان: پردازش ابری، مکان یابی ماشین های مجازی، الگوریتم رقابت استعماری، بهینه سازی مصرف انرژیThe main enabling technology for cloud computing is the use of virtual machines. After making the decision of their placement on hosts, they will be set to run. This process is called virtual machine placement. This process has a great importance on energy consumption and resource wastage avoidance. On the other hand, the growing complexity of cloud infrastructure compounds the problem. In this article, the problem of virtual machine placement is transformed to an optimization problem. The goal is minimizing energy consumption and maximizing the profit of placement, simultaneously. A newly emerged optimization method, called Imperialist Competitive Algorithm is applied in this paper. In addition, a unique method for generating new solutions based on already discovered ones, proposed. Finally the success of the proposed algorithm is confirmed by simulation results and its evaluation is compared with GGA and FFD algorithm.Keywords: Cloud computing, Virtual machine placement, Imperialist competitive algorithm, Energy consumption
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