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Open Access Article
1 - A New Approach to Solve N-Queen Problem with Parallel Genetic Algorithm
Monire Taheri Sarvetamin Amid Khatibi Mohammad Hadi Zahedi -
Open Access Article
2 - Selection and Portfolio Optimization by Mean–Variance Markowitz Model and Using the Different Algorithms
Jamal Bahri Sales Askar Pakmaram Mostafa ValizadehOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not onl MoreOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.Since the objective function of particle swarm algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of this algorithms in the selection of the optimal portfolio. Manuscript profile -
Open Access Article
3 - improving intrusion detection systems by feature reducing based on genetics algorithm and data mining techniques
Mehdi Keshavarzi hossein MomenzadehThe network-based computer systems play critical role in our modern society; so there is highly chance these systems might be target of intrusion and attacks. In order to implement full-scale security in a computer network, firewalls and other intrusion prevention mecha MoreThe network-based computer systems play critical role in our modern society; so there is highly chance these systems might be target of intrusion and attacks. In order to implement full-scale security in a computer network, firewalls and other intrusion prevention mechanisms aren’t always enough and needs other systems called intrusion detection systems. An Intrusion detection system can be set of tools, algorithms and evidence that help to identify, locate and report illegal or not approved activities by the network. Intrusion detection systems can be established by software or hardware systems and each have their own advantages and disadvantages. Because of various characteristics of intrusion detection data, in this research we select effective characteristics using improved genetic algorithm. Then by means of standard data mining techniques, we present a model for data classification.For performance evaluation of this suggested method, we used NSL-KDD database that has more realistic records than other intrusion detection data. Manuscript profile -
Open Access Article
4 - Application of genetic algorithm-Multiple linear regression for prediction of dopamine receptor 4 (D4R) antagonists of alkoxymethyl morpholines
Samira Masoomi Aladezgeh Haniye Ghaffari Jajin Eslam PourbasheerIn this research, by using the structural descriptors and multiple linear regression method, the quantitative structure-activity relationship studies have been carried out to predict the dopamine 4 receptors activity of, alkoxyphenylmorpholine derivatives. Appropriate d MoreIn this research, by using the structural descriptors and multiple linear regression method, the quantitative structure-activity relationship studies have been carried out to predict the dopamine 4 receptors activity of, alkoxyphenylmorpholine derivatives. Appropriate descriptors were selected using the genetic algorithm method. Then a simple and strong model with a high correlation coefficient was built. The results showed that the linear techniques such as multiple linear regression coupled with a suitable variable selection method are able to provide suitable models for predicting the activity of compounds. The values of correlation coefficient (R2) and root mean square error (RMSE) for the training set were 0.729 and 0.285, respectively, and for the test set, they were 0.820 and 0.237, respectively. The presented model showed high statistical parameters that can be used to predict the activity of same compounds. Manuscript profile -
Open Access Article
5 - Genetic Algorithm and ANN for Estimation of SPIV of Micro Beams
M. Heidari -
Open Access Article
6 - Selecting the best wavelet packet pier inspired by biological methods
Alireza Rezaee -
Open Access Article
7 - A new algorithm for data clustering using combination of genetic and Fireflies algorithms
Mahsa Afsardeir mansoure AfsardeirIntroduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret MoreIntroduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret data by exploring the structures using similarities or differences. One of the most widely used clustering methods is the k-means. In this algorithm, cluster centers are randomly selected and each object is assigned to a cluster that has maximum similarity to the center of that cluster. Therefore, this algorithm is not suitable for outlier data since this data easily changes centers and may produce undesirable results. Therefore, by using optimization methods to find the best cluster centers, the performance of this algorithm can be significantly improved. The idea of combining firefly and genetics algorithms to optimize clustering accuracy is an innovation that has not been used before.Method: In order to optimize k-means clustering, in this paper, the combined method of genetic algorithm and firefly worm is introduced as the firefly genetic algorithm.Findings: The proposed algorithm is evaluated using three well-known datasets, namely, Breast Cancer, Iris, and Glass. It is clear from the results that the proposed algorithm provides better results in all three datasets. The results confirm that the distance between clusters is much less than the compared approaches.Discussion and Conclusion: The most important issue in clustering is to correctly determine the cluster centers. There are a variety of methods and algorithms that performs clustering with different performance. In this paper, based on firefly metaheuristic algorithms and genetic algorithms a new method has been proposed for data clustering. Our main focus in this study was on two determining factors, namely the distance within the data cluster (distance of each data to the center of the cluster) and the distance that the headers have from each other (maximum distance between the centers of the clusters). In the k-means algorithm, clustering is not accurate since the cluster centers are selected randomly. Employing firefly algorithms and genetics, we try to obtain more accurate centers of the clusters and, as a result, correct clustering. Manuscript profile -
Open Access Article
8 - An Economic Design of Combined Double Sampling and Variable Sample Size X ̅ Control Chart
Saeed Khaki Niloufar Ghanbari Mir Mahdi Seyed Esfehani -
Open Access Article
9 - Staff Scheduling by a Genetic Algorithm
Ahmad Reza Tahanian Maryam Khaleghi -
Open Access Article
10 - تعیین کیفیت آب در طول مسیر رودخانه با استفاده از شبکههای عصبی مصنوعی تکاملی (مطالعه موردی رودخانه کارون بازه شهیدعباسپور- عرب اسد)
محمد نیکو مهدی نیکو تیمور بابائی نژاد آزاده امیری قدرت الله رستم پوررودخانهها به عنوان اصلی ترین منبع تأمین کننده نیاز شرب، کشاورزی و صنعت از اهمیت خاصی برخوردار هستند. از طرفی کیفیت آب از لحاظ شرب نیز در بین پارامترهای کیفی مهم ترین متغیر میباشد. لذا بررسی و پیش بینی تغییرات پارامترهای کیفی در طول یک رودخانه، یکی از اهداف مدیران و بر Moreرودخانهها به عنوان اصلی ترین منبع تأمین کننده نیاز شرب، کشاورزی و صنعت از اهمیت خاصی برخوردار هستند. از طرفی کیفیت آب از لحاظ شرب نیز در بین پارامترهای کیفی مهم ترین متغیر میباشد. لذا بررسی و پیش بینی تغییرات پارامترهای کیفی در طول یک رودخانه، یکی از اهداف مدیران و برنامه ریزان منابع آب، میباشد. در این راستا تعداد زیادی مدلهای کیفیت آب، در زمینه مدیریت بهتر برای حفظ کیفیت آب، گسترش یافته است. در این میان مدلهای شبکه عصبی مصنوعی که با الهام از ساختار مغز بشر عمل مینمایند، به عنوان گزینهای برتر، مورد تحقیق و بررسی قرار میگیرد. این تحقیق بر روی رودخانه کارون، بزرگترین رودخانه کشور و با استفاده از پارامترهای اندازه گیری شده در ایستگاههای موجود در طول رودخانه (بازه شهیدعباسپور- عرب اسد) انجام شده است. بدین منظور، دبی، ماه، طول رودخانه و پارامترهدایت الکتریکی اندازه گیری شده در ایستگاههای شهیدعباسپور، پل شالو، گتوندو عرب اسد به عنوان ورودیهای مدل، در نظر گرفته شد. با استفاده از مدل شبکه عصبی، نسبت جذب سدیم (SAR) و کل املاح محلول (TDS) اندازه گیری شده در همان ایستگاهها نیز پیش بینی میگردد. از جمله مواردی که در این تحقیق به عنوان یک روش جدید استفاده شده است،تعیین شاخصهای کیفی آب، در چند ایستگاه به صورت هم زمان میباشد. به منظور بهینه کردن هرکدام ازمدلهای شبکه عصبی مصنوعی، از الگوریتم ژنتیک استفاده گردید. نتایج نشان میدهد که مدلشبکه عصبی مصنوعی انتخاب شده، نسبت به مدلهای آماری رگرسیون غیرخطی از توانایی، انعطاف پذیری و دقت بیشتری در پیش بینی کیفیت آب در رودخانه برخوردار میباشد. Manuscript profile -
Open Access Article
11 - Solving N-Queen Problem Using Global Parallel Genetic Algorithm
Monire Taheri Sarvetamin Amid Khatibi BardsiriGreat efforts were made to solve uncertain hybrid optimization problems in the past few decades. The n-Queen problem is one of these problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime an MoreGreat efforts were made to solve uncertain hybrid optimization problems in the past few decades. The n-Queen problem is one of these problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime and are not acceptable in terms of space and memory complexity. In this study, parallel genetic algorithms are proposed to solve n-Queen problem. Parallelizing island genetic algorithm and the Cellular genetic algorithm was implemented and run. The results show that this algorithm has the ability to find related solutions to this problem. The algorithms are not only faster but also they lead to better performance even without the use of parallel hardware and just running on one core processor. Good comparisons were made between the proposed method and serial genetic algorithms in order to measure the performance of the proposed method. The experimental results show that the algorithm has high efficiency for large-size problems in comparison with genetic algorithms, and, in some cases, it can achieve superlinear speedup. The proposed method, in the present study, can be easily developed to solve other optimization problems. Manuscript profile -
Open Access Article
12 - Proposing a New Genetic Algorithm Multi-capacity to Solve the Multi-Storage Routing problem with Multi-capacity Vehicles
Hossien Afzali Gholam Reza Einy Sarkalleh Mojtba Khademy Nejad Elnaz MiandoabchiVehicle routing issues are one of the most common issues in supply chain management and in transport planning. So far, there have been many published academic articles and applied research papers referred in this study. A new innovative algorithm is proposed in this inv MoreVehicle routing issues are one of the most common issues in supply chain management and in transport planning. So far, there have been many published academic articles and applied research papers referred in this study. A new innovative algorithm is proposed in this investigation in order to solve the problem of routing different vehicles with different capacities. The main purpose of this paper is to allocate demand points to each center and determine the best route between the points assigned to each center, as well as determine the best means of transport. The quotes are for each center and the results obtained by the new algorithm are extracted C has been compared with the original algorithms and the results show that this algorithm will be able to compete with innovative algorithms and other interoperability. Manuscript profile -
Open Access Article
13 - Proposing a New Method to Optimize the Routing in the Distribution of Vendors' Goods Using the Internet of Things (IoT)
Mohammad Sadegh Jahan -
Open Access Article
14 - Multi-objective design an agile and value supply chain with multi-objective genetic algorithm approach
Hossein Ali Hassanpour Morteza JabaleA product when at the number of appropriate and timely delivered to the customer at the appropriate time is worth. Today, companies and people are facing the challenges of agility and pivotal values that the use of both approaches and combine them there is no in analyti MoreA product when at the number of appropriate and timely delivered to the customer at the appropriate time is worth. Today, companies and people are facing the challenges of agility and pivotal values that the use of both approaches and combine them there is no in analytic model of the supply chain and the previous literature. In this paper, a study based on linear integer modeling in the field of supply chain network design has been done to address this gap research. Supply chain proposed in three levels of manufacturers, distributors and customers is proposed for multi-objective, multi-product and multi-period. The objective functions is including: Maximizing agility and pivotal values. To solve the Mathematical model is used from GAMS program. Then multi-objective genetic algorithm using non-dominated sorting members of the population proposed and Meta heuristic algorithm and GAMS`s results are Compared to Validate proposed algorithm. In the End, results are analyzed. Manuscript profile -
Open Access Article
15 - The Capacitated Location-Allocation Problem with Interval Parameters
Hassan Shavandi -
Open Access Article
16 - The Optimization of the Effective Parameters of the Die in Parallel Tubular Channel Angular Pressing Process by Using Neural Network and Genetic Algorithm Methods
Amin Armanian Hassan Khademi Zadeh -
Open Access Article
17 - Optimized Designing of the Diametric Network of one or two-layer Diagrid Structure in Dignified Buildings under the Gravity and Lateral Loads
ashkan khodabandelou reza aghajani Diagrid structures as the structural system in dignified buildings, from the applicational point of view, are framed and piped developed structures which decrease the weight of the structure by reducing the cutting limp. The goal of the pr More Diagrid structures as the structural system in dignified buildings, from the applicational point of view, are framed and piped developed structures which decrease the weight of the structure by reducing the cutting limp. The goal of the present article is to optimize the diagrid structures for reducing the weight of structure, determining the number of classifications and the optimize angle and finally comparing the mono and two-layer diagrid structure for selecting the economic option. Through the present research, doing the optimizing diagrid structure by using the extra-creating algorithms, which made the integrated drive electronic necessary, was considered. The Grasshopper graphical programming extension on the Rhino geometric modelling software supplied the algorithmic optimization by making the written program parametric by the genetic algorithm by Galapagos extension. Optimizing in the written software is done by the genetic algorithm by Galapagos extension in base of output results from Karamba analysis structure engine. The optimizing accessed results show that the optimizing angle of the diagrid structures’ members with horizon line for mono-layer diagrid os 64/01 and for two-layer diagrid structure is 65/77. The optimizing weight of the mono-layer diagrid is less than two-layer diagrid which by attention to the simple structure in the rapid time and less energy consuming, the mono-layer diagrid is selected as the affordable option. Manuscript profile -
Open Access Article
18 - Optimal Design of the Diagrid Structural Systems using Improved Genetic Algorithm
Mohammadreza Baradaran Morteza MadhkhanOne of the new structural systems utilized in high-rise structures is diagrid structural systems. In this type of structural system, the columns are removed and the diagrid members are replaced. In other words, in addition to carrying gravity loads, the diagrid members MoreOne of the new structural systems utilized in high-rise structures is diagrid structural systems. In this type of structural system, the columns are removed and the diagrid members are replaced. In other words, in addition to carrying gravity loads, the diagrid members also control lateral loads and there is no need to design a lateral load-bearing system for the structure. The buildings built with this system, in addition to innovation in the structural system, have a beautiful and unique architectural design. One of the most important and effective design parameters in diagrid structures is determining the angle of diagrid members. This angle directly affects the weight of the structure. In the present research, the angle of diagrid members has been studied and evaluated. For this purpose, a 24-storey building has been analyzed and optimized. In order to optimize the structure, an improved genetic algorithm has been used. In this regard, using the genetic algorithm, the optimal weight of the frame at different angles for the diagrid structural system as well as the structural system with mega bracing is determined and the optimal weight is presented. The weight of the diagrid structure was compared with the weight of the mega bracing system. The results show that the optimal angle of the diagonal members in the mega bracing system is approximately 36 degrees, while this angle is approximately 65 degrees for the diagrid system. Manuscript profile -
Open Access Article
19 - The Introduction of a Heuristic Mutation Operator to Strengthen the Discovery Component of XCS
Ahmad Reza Pakraei Kamal Mirzaie -
Open Access Article
20 - An Efficient Genetic Algorithm for Task Scheduling on Heterogeneous Computing Systems Based on TRIZ
Mehdi Akbari -
Open Access Article
21 - Multi-layer Perceptron Neural Network Training Based on Improved of Stud GA
Firozeh Razavi Faramarz Zabihi Mirsaeid Hosseini Shirvani -
Open Access Article
22 - Coverage Improvement Using GLA (Genetic Learning Automata) Algorithm in Wireless Sensor Networks
Shirin Khezri Amjad Osmani Behdis Eslamnour -
Open Access Article
23 - Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry
Arman Izadi Ali mohammad Kimiagari -
Open Access Article
24 - Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry
Arman Izadi Ali Mohammad Kimiagari -
Open Access Article
25 - Economic design of x¯ control charts considering process shift distributions
Vijayababu Vommi Rukmini V. Kasarapu -
Open Access Article
26 - Economic design of Hotelling’s T2 control chart on the presence of fixed sampling rate and exponentially assignable causes
Ehsan Bahiraee Sadigh Raissi -
Open Access Article
27 - The Least Cost Design of Water Distribution Networks Using Water Quality Constraints
M. Tabesh M. Zabihi M. Dini -
Open Access Article
28 - Detection of Autism with Electroencephalographic Signals and Comparison with Healthy People Using Genetic Algorithm Network
Faeze Asadi Bahram Kimia Ghalam -
Open Access Article
29 - The Comparison of Financial Crisis Prediction Strength of Different Artificial Intelligence Techniques
Zahra Pourzamani Hassan kalantariRapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different arti MoreRapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different artificial intelligence techniques(linear and nonlinear genetic algorithm and neural network). Based on available information and statistics, of all companies listed in Tehran Stock Exchange, 72 companies have been subject to Article 141 trade law and 72 companies have not been subject to this Article was elected. Results of Mc-Nemar test for genetic algorithms techniques and neural network showed that there are not significant differences between linear and nonlinear genetic algorithms with neural network. Although the predictive accuracy of nonlinear genetic algorithm(90%) and linear genetic algorithms(80%) is more than of the neural network(70%) but this difference is not statistically significant. Manuscript profile -
Open Access Article
30 - Adaptive Approximate Record Matching
Ramin Rahnamoun -
Open Access Article
31 - Optimal Path Diagnosis by Genetic Algorithm for NoCs
Setareh Shafaghi Reza Sabbaghi-Nadooshan -
Open Access Article
32 - Intrusion Detection in Wireless Sensor Networks using Genetic Algorithm
Elham Yazdankhah Fardad Farokhi Reza Sabbaghi-Nadooshan -
Open Access Article
33 - Selection and Portfolio Optimization by Genetic Algorithms using the Mean Semi-Variance Markowitz Model
Asgar Pakmaram jamal Bahri Sales Mostafa ValizadehOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not onl MoreOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society. In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this. In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk. Since the objective function of genetic algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of these algorithms in the selection of the optimal portfolio. Manuscript profile -
Open Access Article
34 - Applying Multi objective Genetic Algorithms in Portfolio Optimization by Technical Indicators
Hamidreza Mirzaei Ahmad Khodamipour Omid PourheidariRisk-return tradeoff and its analysis in alternative investments as a classic goal of finance have been the main subject of many researches in financial management. The use of technical indicators is a portfolio management tools. This research aims to use these indicato MoreRisk-return tradeoff and its analysis in alternative investments as a classic goal of finance have been the main subject of many researches in financial management. The use of technical indicators is a portfolio management tools. This research aims to use these indicators in mining stocks trading rules. The period of investigation is from beginning of 1388 until the end of 1393 and the sample of study is including 216 companies listed in TSE. In the period from 1388 to 1390 by using technical indicators and genetic algorithm with aim for maximize return and minimize risk, we obtain a model for portfolio optimization and in the period from 1391 to 1393 this model was used in portfolio management. In order to evaluate this model, the results were compared with the market index and found that by using technical indicators can outperform the market. Manuscript profile -
Open Access Article
35 - Fuzzy – neural model with hybrid genetic algorithms for stock price forecasting in auto industry in Tehran security exchange
ehsan Sadeh reza Ehtesham Rasi ali Sheidaei NarmigiSelection of appropriate time and price in trading stocks has an important role in investment decisions on profit and loss of investors in capital markets. Nonlinear intelligent systems, such as artificial neural networks, fuzzy- neural networks and genetic algorithms, MoreSelection of appropriate time and price in trading stocks has an important role in investment decisions on profit and loss of investors in capital markets. Nonlinear intelligent systems, such as artificial neural networks, fuzzy- neural networks and genetic algorithms, would be used to forecast stock prices motions. In this article,a model of stock prices motions has been designed using Adaptive Neuro- Fuzzy Inference System (ANFIS)integrated with genetic algorithm, in which two different groups of fundamental and technical variables have been employed as model inputs. According to Model outputs,the rate of forecasting errors in both groups of inputs is not significant and these systems are able to forecast daily stock prices. The Mann-Whitney test has been used to measure the accuracy of models and it was found that there is no significant difference between results of prices forecasted in both methods. Both methods are able to forecast next day price with an insignificant error provided that at least one of the inputs in both methods has a linear dependence with price, . Also, results show that these systems do not work properly to forecast prices of high volatility stocks Manuscript profile -
Open Access Article
36 - Forecast earnings management based on adjusted Jones model using Artificial Neural Networks and Genetic Algorithms
Khosro Faghani Makrani S. Hasan Salehnezhad Vahid AminIn recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and ge MoreIn recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and genetic algorithms - neural network hybrid model as a successful model to predict earnings management based on adjusted Jones model were used in the Tehran Stock Exchange. The sample used in this study is consisted of 570 firm-year between 2008 to 2013. The results showed that neural networks have a high ability to predict earnings management rather than the adjusted Jones linear model. The findings also suggest that, the genetic algorithm through optimizing artificial neural network weights is able to increase power of artificial neural network to predict earnings management. Manuscript profile -
Open Access Article
37 - Neuro-Genetic Structure to valuation of Initial Public Offering
ali rostami Emad Falamarzi sara FaroughiConsidering stock market history, major concerns in the first phase to enter the capital market is that what the right price for the initial public offering and could they convince investors to buy shares. Besides that, there are also investors concerns about the accura MoreConsidering stock market history, major concerns in the first phase to enter the capital market is that what the right price for the initial public offering and could they convince investors to buy shares. Besides that, there are also investors concerns about the accuracy of the pricing stocks. This study uses nonlinear method has resolved this issue. Study provides a model pricing initial public offering of shares on the Tehran Stock Exchange. The research period between 1382 to 1393. Research population 145 enterprises entering the Tehran Stock Exchange in this period of time and the sample of study is according to the condition of the Company and continuous investment of funds and access to company data, were reduced to 103 companies. The proposed network is a neural network optimized the genetic algorithm to determine the price of shares of new companies entering the stock exchange.With a choice of 12 variables affecting the price of initial public offerings and one dependent variable (Initial Public Offering price) suitable model to _ pricing than other linear models presented. The results of the fourth measure, RMSE, MAE, R-SQUARE, U-THEIL reflect the correct pricing proposed model, in most cases. Manuscript profile -
Open Access Article
38 - The Modeling of Exchange Rate Predict in Iran by Using Neural Network Based on Genetic Algorithms and Particle Swarm Algorithm
ali jamali saeed daie karimzadehIn recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weakn MoreIn recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weaknesses points despite its enormous benefits also. In this study, in order to overcome the weaknesses of the network consists of combining artificial intelligence methods with Evolutionary algorithms, means of artificial neural network combined with genetic algorithm (GA) and Particle Swarm algorithm (PSO) to model and daily predict of nominal exchange rates or the exchange rate dollar by Rial in Iran in the period 21.03.2013 to 22.12.2019 is used. This combined model with neural networks method as one artificial intelligence model according to the criteria of MSE , RMSE, MAE, U.Theil compared. The results of this research show the superiority of synthetic neural network model -Particle Swarm algorithm compare to other models of investigation. Manuscript profile -
Open Access Article
39 - طراحی مسیر بهینه یک بازوی مکانیکی ماهر با پایه متحرک برای عبور از میان موانع فضایی
مصطفی غیور مصطفی شریعتینیادر این تحقیق روشی برای طراحی مسیر یک سیستم ربات پایه متحرک فضایی شامل پایه غیرهلونومیک و بازوی سه عضوی در حضور موانع ثابت و متحرک ارائه شده است. در اینجا از توابع پیوسته و هموار مانند توابع چندجملهای به منظور مسیریابی ربات استفاده شده است. روش ارائه شده شامل به دست آور Moreدر این تحقیق روشی برای طراحی مسیر یک سیستم ربات پایه متحرک فضایی شامل پایه غیرهلونومیک و بازوی سه عضوی در حضور موانع ثابت و متحرک ارائه شده است. در اینجا از توابع پیوسته و هموار مانند توابع چندجملهای به منظور مسیریابی ربات استفاده شده است. روش ارائه شده شامل به دست آوردن تاریخچه زمانی حرکت محرکهای ربات میشود که تحت رفتار این محرکها، ربات به پیکربندی نهایی خود میرسد. پایه بهکار رفته در این تحقیق، پایه با رانش دیفرانسیلی است که از انواع پرکاربرد پایههاست. بازوی مکانیکی واقع بر پایه نیز بازوی سه درجه آزادی فضایی است. ترکیب بازو و پایه باعث میشود که ربات در فضای کاری وسیع تری عمل کند. هر چند بررسی این نوع سیستمها شامل بررسی مسئله به نام افزونگی درجات آزادی میشود که به پیچیدگی مسئله میافزاید، ولی افزونگی درجات آزادی در ربات، قابلیتهای ویژهای از نظر کاربردی برای آنها ایجاد میکند. در رباتهای دارای افزونگی درجات آزادی در یک فضای کاری مشخص، مسیرهای متعددی برای ربات وجود دارد. یک راه برای انتخاب یک مسیر مناسب از بین مسیرهای ممکن، انتخاب یک اندیس مناسب و بهینه کردن آن است. نتایج عددی و نمودارها جهت طراحی مسیر بهینه برای یک مجموعه ربات پایه متحرک در حضور موانع با استفاده از روش الگوریتم ژنتیک آورده شده است. موانع بهکار رفته در مسئله نیز فضایی بوده و موانع ثابت و متحرک را شامل میشود. Manuscript profile
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