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نتیجه جستجو - Gravitational search algorithm

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 Real-time data text mining based on Gravitational Search Algorithm
داده کاوی متن در زمان واقعی بر اساس الگوریتم جستجوی گرانشی-2019
Short messages are one of the milestones on the web especially on social media (SM). Due to the widespread circulation of SM, it already turns into excessively painful capturing outmost relevant and significant information for certain users. One of the main motivations of this work is that many users may need an inclusive brief of all comments without reading the entire list of short messages for deci- sion making. In this work, mining in big social media data is formulated for the first time into a multi- objective optimization (MOO) task to extract the essence of a text. Since some users may demand the brief at any moment, several groups of dissimilar short messages are established based on graph coloring mechanism. Six interesting feature are formalized to exhibit more interactive messages. A Gravitational Search Algorithm (GSA) is employed to satisfy several important objectives for generating a concise sum- mary. The problem was picked by using the Normal Boundary Intersection (NBI) mechanism to trade-offamong different features. Additionally, to satisfy real-time needs, an inventive incremental grouping task is modelled to update the existing colors. From exhaustive experimental results, the proposed approach outperformed other strong comparative methods.
Keywords: Data text mining | Swarm Intelligence | Big-data | Gravitational search algorithm | Normal boundary intersection
مقاله انگلیسی
2 Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) – A hybrid expert system for diagnosis of diabetes
الگوریتم جستجوی گرانشی همبسته تکاملی (ECGS) با شبکه عصبی بهینه سازی شده ژنتیکی (GHNN) - یک سیستم متخصص ترکیبی برای تشخیص دیابت-2019
In worldwide 415 million of peoples are affected by diabetics in the year of 2015, that is increased from the year of 2012. Based on the survey, it clearly shows the diabetics are one of the dangerous diseases because it leads to create several risk of early death. Due to the seriousness of the diabetic, it has been detected in early stage by creating expert system. During this process, the expert system has several issues such as accuracy of prediction due to the huge dimension of the diabetic feature that reduce the entire efficiency of the system. So, in this paper introduced the evolutionary correlated gravitational search algorithm (ECGS) for selecting the optimized features. The introduced method analyzes each diabetic feature according to the correlation and mutual information is selected with minimum computation time and cost. The selected features are processed by genetic optimized Hopfield neural network (GHNN) for predicting the diabetic related features effectively. Then the efficiency of the system is implemented using MATLAB tool that utilizes the Pima Indian Diabetic Dataset for analyzing the efficiency of introduced diabetic expert system. The efficiency of the system is evaluated in terms of using mean square error rate, F-measurer, accuracy, confusion matrix and ROC curve.
Keywords: Diabetics | Evolutionary correlated gravitational search | algorithm (ECGS) | Genetic optimized Hopfield neural network | (GHNN) | Pima Indian Diabetic Dataset
مقاله انگلیسی
3 A novel shearer cutting pattern recognition model with chaotic gravitational search optimization
مدل تشخیص الگو برش برش رمان با بهینه سازی جستجوی گرانشی آشفته-2019
The accurate recognition of the shearer cutting pattern is the focus in fully mechanized coal mining. Hence, a new cutting pattern recognition model based on the combination of Relevance Vector Machine (RVM) and Chaotic Gravitational Search Algorithm (CGSA) is proposed. Initially, the motor operation data, including voltage, current and motor speed, are collected as the detection signal and the RVM classifier based on Bayesian framework is chosen for pattern recognition. In order to optimize the parameters in RVM, which has a great influence on the performance of RVM, the optimization algorithm Gravitational Search Algorithm (GSA) is introduced. Finally, the basic GSA is modified into CGSA with the chaotic mapping for increasing the search diversity of the algorithm. The experimental study demonstrates the advantageous performance of the proposed model even without any feature extraction operations.
Keywords: Cutting pattern recognition | Relevance Vector Machine (RVM) | Gravitational Search Algorithm (GSA) | Chaotic mapping
مقاله انگلیسی
4 Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018
Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization
مقاله انگلیسی
5 Optimized controllers for enhancing dynamic performance of PV interface system
کنترل کننده های بهینه برای ارتقای عملکرد پویای سیستم فصل مشترک PV-2018
The dynamic performance of PV interface system can be improved by optimizing the gains of the Proportional–Integral (PI) controller. In this work, gravitational search algorithm and harmony search algorithm are utilized to optimal tuning of PI controller gains. Performance comparison between the PV system with optimized PI gains utilizing different techniques are carried out. Finally, the dynamic behavior of the system is studied under hypothetical sudden variations in irradiance. The examination of the proposed techniques for optimal tuning of PI gains is conducted using MATLAB/SIMULINK software package. The main contribution of this work is investigating the dynamic performance of PV interfacing system with application of gravitational search algorithm and harmony search algorithm for optimal PI parameters tuning.
keywords: Photovoltaic power systems |Gravitational search algorithm |Harmony search algorithm |Genetic algorithm |Artificial intelligence
مقاله انگلیسی
6 Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms
تشخیص جامعه از شبکه های بیولوژیک و اجتماعی: یک تحلیل مقایسه ای از الگوریتم های فرا ابتکاری -2017
In order to analyze complex networks to find significant communities, several methods have been pro posed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang–Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new meth ods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were con ducted using nine real-world complex networks − five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.
Keywords: Metaheuristic optimization algorithms | Community detection | Biological networks | Social networks | Modularity
مقاله انگلیسی
7 A novel data clustering algorithm based on modified gravitational search algorithm
یک الگوریتم نوین برای خوشه بندی داده ها برمبنای الگوریتم جستجوی گرانشی اصلاح شده-2017
Data clustering is a popular analysis tool for data statistics in many fields such as pattern recognition, data mining, machine learning, image analysis, and bioinformatics. The aim of data clustering is to represent large datasets by a fewer number of prototypes or clusters, which brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. In this paper, a novel data clustering algorithm based on modified Gravitational Search Algorithm is proposed, which is called Bird Flock Gravitational Search Algorithm (BFGSA). The BFGSA introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main steps including initialization, identification of the nearest neighbors, and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The performance of the proposed algorithm is evaluated through 13 real benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), K-means, and other four clustering algorithms from the literature. The simulation results indicate that the BFGSA can effectively be used for data clustering.
Keywords: Gravitational search algorithm | Learning algorithm | Collective behavior | Data clustering | Clustering Validation | Nature-inspired algorithm
مقاله انگلیسی
8 جستجوی گرانشی هیبریدی جدید و الگوریتم جستجوی الگو برای کنترل فرکانس بار سیستم قدرت غیر خطی
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 18 - تعداد صفحات فایل doc فارسی: 46
در این مقاله، تکنیک الگوریتم جستجوی گرانشی هیبریدی (GSA) و جستجوی الگو (PS) برای کنترل فرکانس بار (LFC) سیستم قدرت چند ناحیه ای پیشنهاد داده شده است. ابتدا، معیارهای خطای معمولی مختلف شامل پارامترهای کنترل کننده ی PI برای سیستم قدرت دو ناحیه ای می شود که توسط GSA بهینه سازی شده و تاثیر توابع هدف بر روی نقش سیستم تحلیل و بررسی می شود. پارامتر های کنترل GSA توسط چند بار ران کردن (اجرا کردن) الگوریتم برای متغیر پارامتر کنترل، تنظیم می شود. تغییرات در تابع هدف و ساختار کنترل کننده معرفی می شوند و پارامترهای کنترل کننده توسط روش پیشنهادی ترکیبی GSA و PS (Hgsa-ps) بهینه سازی می شوند. برتری روش پیشنهادی توسط مقایسه ی نتایج منتشر شده ی اخیر با تکنیک های بهینه سازی اکتشافی مانند الگوریتم های کرم شب تاب (FA)، الگوریتم تفاضلی (DE)، الگوریتم غذایابی باکتری (BFOA)، بهینه سازی اجتماع ذرات (PSO)، ترکیب BFOA-PSO,NSGA-II و الگوریتم ژنتیک (GA) برای همان سیستم قدرت به هم پیوسته؛ به اثبات رسیده است. علاوه بر این، حساسیت تحلیل و بررسی توسط پارامترهای سیستم متغیر و عملکرد شرایط بار از مقدار نامی انجام شده است. همچنین روش پیشنهادی قابل تعمیم به سیستم قدرت بازگرم کن حرارتی دو ناحیه ای با در نظر گرفتن قیدهای فیزیکی نظیر توربین بازگرم کن، قید نرخ تولید (GRC) و باند مرده ی گاورنر (GDB) به صورت غیر خطی است. در نهایت برای اثبات الگوریتم پیشنهادی برای مقابله با غیر خطی و نا برابری نواحی به هم پیوسته با ضرایب کنترلی مختلف، مطالعه به سیستم سه ناحیه ای نابرابر غیر خطی گسترش یافته است و پارامتر های کنترل کننده در هر ناحیه توسط تکنیک پیشنهادی hGSA-PS بهینه سازی شده است.
کلمات کلیدی: کنترل فرکانس بار (LFC) | کنترلگر PID | الگوریتم جستجوی گرانشی (GSA) | جستجوی الگو (PS) | مسئول باند غیر فعال غیر خطی | محدودیت نرخ نسل (GRC)
مقاله ترجمه شده
9 برنامه ریزی توان راکتیو با دستگاه های FACTS با استفاده از الگوریتم جستجوی گرانشی
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 19
در این مقاله، الگوریتم جستجوی گرانشی (GSA) بعنوان روش بهینه سازی در برنامه ریزی توان راکتیو با استفاده از دستگاه های (سیستم انتقال AC انعطاف پذیر) FACTS استفاده می شود. مسئله برنامه ریزی بعنوان مسئله بهینه سازی تک هدفه فرمو ل بندی می شود که در آن تلفات توان واقعی و انحرافات ولتاژ باس تحت شرایط بارگذاری به حداقل می رسند. الگوریتم بهینه سازی مبتنی بر GSA و روش های بهینه-سازی ازدحام ذرات (PSO) در سیستم باس IEEE 30 به کار می روند. همچنین نتایج نشان می دهد که GSA ابزار بسیار موثری برای برنامه ریزی توان راکتیو است.
کلمات کلیدی: GSA | تلفات توان واقعی | برنامه ریزی توان راکتیو | FACTS | بهینه سازی
مقاله ترجمه شده
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