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Open Access Article
1 - An Improved Imperialist Competitive Algorithm based on a new assimilation strategy
Seyed Mojtaba Saif -
Open Access Article
2 - Investigating the Efficiency of Evolutionary Optimization Methods in Achieving Architectural and Construction Objectives
Maryam Sadeghian Akram HosseiniThe increasing popularity of the optimization approach in different sciences has led architects to use them tomachieve various objectives in designing and cons tructing buildings. However, the functions, advantages, and limitations for each of these optimization s MoreThe increasing popularity of the optimization approach in different sciences has led architects to use them tomachieve various objectives in designing and cons tructing buildings. However, the functions, advantages, and limitations for each of these optimization s trategies are scarcely known, due to their newness in architecture and cons truction fields Optimization algorithms are classified into three categories: determinis tic, heuris tic, and meta-heuris tic algorithms. Meta-heuris tic algorithms, are more efficient and categorized into three main groups: evolutionary computing, swarm intelligence, and physics-related algorithms. Mos t of the s tudies conducted on optimization algorithms, in this field, are on the application of one of the optimization algorithms in the design of a particular project. Limited research has been done in coordination with the subject of this s tudy, inves tigating the application of these algorithms in a specific field. After reviewing the his tory and literature of the subject, to discuss how optimization methods are used in architecture, 77 related articles and theses that used optimization methods have been reviewed through scholar works published since 1996 (the firs t publications in this field) up to now. Selected research was analyzed using the textual content analysis method to determine "the efficiency ofevolutionary optimization methods in achieving architectural and cons truction objectives" as the main research question; there were also several sub-ques tions on the way to answer the main ques tion: Which architectural objectives are mos t achievable by using optimization algorithms? Which types of optimization algorithms are appropriate for architectural objectives? Which building functions have the mos t potential for using optimization methods? Which researchers conduct and support the research of evolutionary algorithms in building issues? Optimization algorithms have been undertaken to solve design problems for six different objectives: mass design and urban access, cons truction and cos t management, building’s s tructural design, energy issues, building form generation and space planning. Various design variables have been defined to search for optimal response to each of the objectives. Among these objectives, the highes t application of optimization algorithms is related to spatial planning optimization in residential buildings and energy optimization in official ones. The analysis of the publications demons trates that various methods of meta-heuris tic optimization have been used over time to solve architectural problems. Genetic Algorithm is the mos t widely used one in architectural optimization, and particle swarm optimization is the mos t common method in swarm intelligence based research. The review of s tudies indicates the predominantly theoretical attention of architectural scholars to this issue. Given the dis tance between the research and the implementation phase, architects should work more closely with researchers in other fields, especially those in computer science, to approach the implementation s tage. However, the development of each of these areas requires the improvement of previous methods and research into how other algorithms, such as swarm intelligence based ones, can be used to solve design problems in architecture. The development of user-friendly software with a graphical user interface for a better grasp of the design process and results can affect architects' usage of evolutionary algorithms as a design method. Manuscript profile -
Open Access Article
3 - Developing a Stock Technical Trading System Integrating MLP Neural Network with Evolutionary Algorithms
Alireza Saranj Ahmadreza Ghasemi Asghar Eram Reza TehraniStock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms MoreStock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms (i.e., GA, ACOR, and PSO). In order to select the final variables as the selected features, a return comparison of each indicator ratings was used based on tradings. Finally, the performance of each model is tested in comparison with the buy and hold strategy. The results show that the evolutionary learning algorithms significantly outperform the benchmark models in terms of the average return and the hybrid MLP_PSO model outperforms others. Manuscript profile -
Open Access Article
4 - Optimization of Drainage Design Parameters with the Aim of Reducing Environmental Damage in Steady-State C onditions
Hamed Mazandarani Zadeh Rahime Zadesh Pargo Peyman Daneshkar ArastehBackground and Objective: Diameter, insertion depth and spacing of drainage pipes are three crucial variables in the design of underground drainage network. Effluents have a great potential to leave lots of damage on the environment. The proper selection of design varia MoreBackground and Objective: Diameter, insertion depth and spacing of drainage pipes are three crucial variables in the design of underground drainage network. Effluents have a great potential to leave lots of damage on the environment. The proper selection of design variables can lead to reducing the environmental damage. The purpose of this paper is to provide a model for selecting optimal design parameters for underground drainage systems to reduce environmental damage, in a way that after the discharge of drainage to the river, river salinity concentration does not exceed the acceptable limit. Method: For this purpose, maximization of difference between drainage water salinity and acceptable limit was considered as the objective function. Genetic Algorithm (GA), kind of evolutionary algorithm, has been used to simulate the transmission and the salt Hooghoudt model was used also. In Hooghoudt model water transition to drainage is modeled in two upper and lower individual part. In order to evaluate the proposed model, an agro-industrial unit Salman Farsi was chosen as case study. Matlab software was employed to program the formula and algorithm which has been used in this research, including Hooghoudt salinity transfer simulation function and GA algorithm optimization. Findings: Results show that the pipe depth is complying with minimum allowable depth. In other words, since the objective function of the model is to achieve minimal environmental damage, the minimum depth of installation is generally chosen. Optimum diameter, insertion depth and spacing have been obtained 1.3, 0.1 and 34.3 respectively. The results of the sensitivity of the model to change of the two basic assumptions, minimum allowable depth and stabilize the water table depth stabilizing, shows by increasing the allowable minimum depth of installation, drainage spacing increases and reducing the depth of the water table stabilizing will increase the drainage intervals and leads to increasing the concentration of drainage water discharged to the environment. Discussion and Conclusion: In this study and by using information about the Salman Farsi agro-industry company, to reduce the environmental damage caused by drainage projects, installation depth of drainage should be equal to the minimum allowable depth. Manuscript profile -
Open Access Article
5 - Selection of optimal portfolio by using improved Non-Dominated Sorting Genetic Algorithm and Evolutionary Algorithm Strength Pareto By taking risk on the basis of conditional value at risk
Mojtaba Moradi Maryam GhavidelPortfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or s MorePortfolio selection problem is one of the most important economic issues. The right combination of stock or other asset portfolio is that an investor pays to buy it. Selection of an optimal portfolio is based on the principle that the investor decides to accept one or several investments among different investment depending on the tolerance of risk and expected a reasonable amount of stock returns. In this study, improved Non-Dominated Sorting multi-objective genetic algorithms and Evolutionary Algorithm Strength Pareto are used to create an optimum portfolio. These algorithms are improved version of their previous versions and have a better solution than its previous versions. The value of the portfolio and its risk, as optimization purposes and conditional value at risk as the basis risk, have been used. Two applied conditions consider to Portfolio and shown that the Evolutionary Algorithm Strength Pareto has better results than the Non-Dominated Sorting Genetic Algorithm II. Manuscript profile -
Open Access Article
6 - Fuzzy portfolio selection under down risk measure by hybrid intelligent algorithm
Hojat Ansari Adel Behzadi Mostafa EmamdoostPortfolio optimization is one of more important problems in financial area. The classic model consider that stocks is random variable with symmetric probability density function. But in real world, forecasting stock condition always faced with uncertainty and we need in MorePortfolio optimization is one of more important problems in financial area. The classic model consider that stocks is random variable with symmetric probability density function. But in real world, forecasting stock condition always faced with uncertainty and we need insert human factors in our forecasting. Fuzzy logic is one of methods that we can use this to model this condition. On other hand, experimental studies show that assets return isn’t normal and symmetric, so we should use down risk measure such as semi variance and semi absolute deviation. In this research we consider two point in portfolio selection problem. Then we use two intelligent method based genetic and deferential evolutionary algorithm for solving the models. Making use of Tehran Stock Exchange data, it is concluded that considering semi absolute deviation has higher efficiency than semi variance model and intelligent method based deferential evolutionary algorithm has higher efficiency from intelligent method based genetic algorithm. Manuscript profile -
Open Access Article
7 - Management of large energy storage power plants: optimization of charging and discharging with cuckoo search algorithm
Behnam Motalebinejad Majid Hosseina Mojtaba Vahedi Mahmoud Samiei MoghaddamThey are directly integrated into smart distribution networks and can supply stored energy during peak demand periods, while absorbing and storing energy during periods of low demand. This capability helps maintain a balance between supply and demand in power grids, pre MoreThey are directly integrated into smart distribution networks and can supply stored energy during peak demand periods, while absorbing and storing energy during periods of low demand. This capability helps maintain a balance between supply and demand in power grids, preventing voltage fluctuations and the inability to meet peak loads during high-demand hours. Thanks to technological advancements, it is now possible to upgrade large-scale energy storage facilities. The modern architecture and technology of these facilities facilitate the efficient utilization of renewable energy sources, significantly reducing energy costs and increasing energy efficiency. Additionally, through the use of artificial intelligence algorithms and optimization techniques, the performance and operations of large-scale energy storage facilities can be enhanced. This article focuses on the management of large-scale energy storage facilities, introducing innovative measures that include constraints on the number of charge and discharge processes. Furthermore, the use of the advanced Fakete search algorithm is employed as a powerful and efficient method for solving the proposed model. This algorithm has the capability to find global optimal solutions and can significantly improve the efficiency and profitability of large-scale energy storage facilities. Simulation results demonstrate that adopting this approach in managing large-scale energy storage facilities leads to significant economic impacts. These impacts include reduced energy costs, increased efficiency, greater independence from fossil fuel resources, the preservation of grid stability, and improved performance of the power transmission system. Manuscript profile -
Open Access Article
8 - Energy-Efficient Wireless Sensor Networks Using Flat Cluster-based Routing Protocol and Evolutionary Algorithms
masoud negahdari Marziye DadvarWireless sensor networks have a large number of limited-energy sensor nodes dispersed in a finite area. Most node energies are used to send data to the central station. Due to the energy constraints in this type of grid, increasing life expectancy has always been a conc MoreWireless sensor networks have a large number of limited-energy sensor nodes dispersed in a finite area. Most node energies are used to send data to the central station. Due to the energy constraints in this type of grid, increasing life expectancy has always been a concern with decreasing energy consumption. The aim of this study is to provide surface clustering based on genetic algorithm in order to increase the life span of these networks. In proposed surface clustering, the geographic area is divided into three levels according to the radio range and the clustering of the nodes of each level is done individually. The cluster heads use more energy than other nodes to send information, so the proposed algorithm aims to reduce the number of cluster heads in order to increase the network lifetime. Finally, by changing the clusters in each routing round, there is a greater energy balance between the nodes. The results from the experiments indicate the superiority of the proposed algorithm in transmitting messages and network lifetimes over other similar protocols. Manuscript profile -
Open Access Article
9 - کارآیی مدل برنامه ریزی ژنتیک در شبیه سازی فرآیند بارش- رواناب (مطالعه موردی : حوضه آبریز رودخانه خرم آباد)
حمیدرضا باباعلی زهره رامک رضا سپهوندپیش­بینی میزان دبی رودخانه یکی از مسایل مهمِ مهندسی منابع آب است؛ این موضوع از نظر برنامـه­ریـزی، مـدیریت، و سیاست­گذاری منابع آبی در راستای توسعة اقتصادی و زیستمحیطی به­ویژه در کشوری مثل ایران، با منابع آبی محدود اهمیت بسیار زیادی دارد. آگاهیازچگونگیارت Moreپیش­بینی میزان دبی رودخانه یکی از مسایل مهمِ مهندسی منابع آب است؛ این موضوع از نظر برنامـه­ریـزی، مـدیریت، و سیاست­گذاری منابع آبی در راستای توسعة اقتصادی و زیستمحیطی به­ویژه در کشوری مثل ایران، با منابع آبی محدود اهمیت بسیار زیادی دارد. آگاهیازچگونگیارتباطبینبارندگیورواناب حوضه­هایآبریزبخشجدانشدنیمطالعاتطرح­هایآبی می­باشد. فقدانداده­هایکافیبارش-رواناببهدلیلنبود ایستگاه­هایآبسنجیمناسب،اهمیتبه­کارگیری روش­های نامستقیم و الگوریتم­های فراکاوشیرابرایبرآوردمیزانروانابحوضه­های آبریزبیشازپیشنمایانمی­سازد. در این تحقیق از مدل برنامه­ریزی ژنتیک به­منظور شبیه­سازی فرآیند بارش-رواناب حوضه آبریز رودخانه خرم­آباد استفاده شده است و برای معرفیالگوهاوشناساییبهترینالگویحاکمبرماهیت جریان، با استفاده از توابع برازش و انجام فرآیندهای توسعه­ای و تکرار به منظور یافتن تعداد تکرار بهینه، همه داده­های دوره آماری به دو دسته آموزش و آزمایش(52% آموزش و 48% آزمایش) تقسیم شدند و برنامه برای 1000 تکرار اجرا گردید. همچنین جهت ارزیابی روابط حاصله از مدل شبیه­ساز، از شاخص­های جذر میانگین مربعات خطا (RMSE)،میانگین خطای مطلق(MSE) وضریب تعیین (R2) استفاده شده است. بررسی­های انجام شده حاکی از آن است که فرمول استفاده شده شماره 3 بیشترین انطباق را با داده­های مشاهداتی دارد. بنابراین پیشنهاد میشود جهت مطالعات بارش- رواناب این حوضه از فرمول ذکر شده استفاده گردد. نتیجه این تحقیق مدل برنامه­ریزی ژنتیک را یک روش صریح و دقیق برای پیش­بینی جریان رودخانه­ در حوضه آبریز رودخانه خرم­آباد پیشنهاد می­نماید. Manuscript profile -
Open Access Article
10 - Remaining useful life estimation of mechanical systems by mixed method of mathematical method and evolutionary computational framework
fatemeh mehreganAn accurate prediction of the remaining useful life of the equipment is necessary for use, repairs and maintenance. Useful life prediction has been widely used, while the data obtained from it is not functional in different conditions. Many data-driven algorithms have b MoreAn accurate prediction of the remaining useful life of the equipment is necessary for use, repairs and maintenance. Useful life prediction has been widely used, while the data obtained from it is not functional in different conditions. Many data-driven algorithms have been proposed and good results have been obtained in the field of predictive troubleshooting. Therefore, in this article, the relevant parameters are optimized using the meta-heuristic algorithm, so that the moving time window is used along with the mathematical model. Setting parameters related to data in the optimization framework allows the use of simple models such as neural networks with a small number of hidden layers and a small number of neurons in each layer, which can be used in environments with limited resources such as embedded systems. To evaluate the effectiveness of the proposed method, the root mean square error scoring index and useful life health score have been used. For this purpose, a random data set has been considered and the results show the acceptability of the method. Manuscript profile -
Open Access Article
11 - Participative Biogeography-Based Optimization
Abbas Salehi Behrooz Masoumi -
Open Access Article
12 - PSPGA: A New Method for Protein Structure Prediction based on Genetic Algorithm
Arash Mazidi Fahimeh Roshanfar -
Open Access Article
13 - A New Method for the Residues Cost Allocation and Optimization of a Cogeneration System Using Evolutionary Programming
S.M. Seyyedi -
Open Access Article
14 - Designing PSS and SVC Parameters simultaneously through the Improved Quantum Algorithm in the Multi-machine Power System
Amir Kazemi Zahrani Moein ParastegariAs to the importance of power system performance in terms of quality, and stability, through flexible AC transmission system (FACTS) devices in power networks and coordinating these devices through power system stabilizers (PSSs) has gained a great acceptance. According MoreAs to the importance of power system performance in terms of quality, and stability, through flexible AC transmission system (FACTS) devices in power networks and coordinating these devices through power system stabilizers (PSSs) has gained a great acceptance. Accordingly, the problem of coordinated design of PSS and static var compensator (SVC) parameters in multi-machine power systems is introduced and solved through the improved quantum method. In previous studies PSS is designed for damping small-signal oscillations of the power system. To damp large-signal oscillations, PSS should be designed in accordance with other devices like SVC. Therefore to reach overall stability of power system, the Quantum-inspired Evolutionary Algorithm is applied here to determine PSS parameters and SVC in a coordinated manner. This proposed method is applied in determining PSS parameters and SVC of Kundur’s four-machine power systems and the New England 39-bus system. Simulation results reveal the effective performance of this proposed method in comparison with Particle Swarm Optimization (PSO) and Bacteria Foraging Optimization (BFO) methods. Manuscript profile -
Open Access Article
15 - Operation of Micro-Grid for Provide Clean Energy Constrained to System Optimal Reliability
Hosein Hasan Shahi Mehdi Nafar Mohsen SimabIn this paper, the problem of micro-grid (MG) energy management in the presence of distributed generations (DGs) and active loads (ALs) considering operation, economic, pollution and reliability is presented. This scheme includes objective function that is minimized the MoreIn this paper, the problem of micro-grid (MG) energy management in the presence of distributed generations (DGs) and active loads (ALs) considering operation, economic, pollution and reliability is presented. This scheme includes objective function that is minimized the summation of expected operation cost of MG and DGs, expected pollution cost and outage cost in the N-1 contingency. This problem is constrained to AC power flow equations, MG operation and reliability limits, and operation formulation of DGs and ALs including the demand response program (DRP) and battery. Also, this paper uses the stochastic programming to model uncertainties of load, energy price, renewable DGs (RDGs) generation power and Availability of MG Equipment. Then, to achieve unique reliable optimal solution, it uses hybrid solver of ant-lion optimizer (ALO) and crow search algorithm (CSA). Finally, by implementing of the proposed strategy on a standard MG and obtain numerical results, the capability of the scheme in improving technical and economic indices of the MG along with procuring clean and reliable energy is confirmed. Manuscript profile -
Open Access Article
16 - Robust Planning of the Islanded Hybrid System Including Renewable and Non-Renewable Sources and Stationary and Mobile Storages
Farshad KhalafianIn this paper, the robust planning of the islanded hybrid system (IHS) to create an integrated system with wind turbine, photovoltaic, diesel generator, stationary (battery) and mobile (electric vehicles) storages is presented. The proposed scheme minimizes the planning MoreIn this paper, the robust planning of the islanded hybrid system (IHS) to create an integrated system with wind turbine, photovoltaic, diesel generator, stationary (battery) and mobile (electric vehicles) storages is presented. The proposed scheme minimizes the planning cost (including construction, maintenance, and operation) of the mentioned sources and storages, and environmental pollution level. This problem is constrained to operation and planning model of the different sources and storages, and power balance constraint in IHS. The proposed scheme is formulated in the Pareto optimization framework based on method of the summation of weighted functions. Also, the bounded uncertainty-based robust optimization (BURO) is used to model the uncertainties of load, renewable power, and energy of mobile storage. Then, the hybrid evolutionary algorithm according to composition of Krill Herd Optimization (KHO) and Grey Wolf Optimization (GWO) algorithms obtains an optimal solution including low standard deviation in the final response. Finally, it is seen that the proposed scheme has a suitable capability in the planning of the proposed system according to economic and environmental viewpoints based on obtained numerical results. Manuscript profile -
Open Access Article
17 - Increase the Efficiency of the Offloading Algorithm in Fog Computing by Particle Swarm Optimization Algorithm
Seyed Ebrahim Dashti Hoasain ZareEdge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage op MoreEdge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage operations take place close to objects and data sources. In order to reduce latency and network traffic between end devices and cloud centers, groups at the edge have processing capabilities, perform a large number of processing and computing tasks, including data processing, temporary storage, device management, decision making, and privacy protection. Since the number of edge devices is large, there must be a mechanism to select these tasks and offload them to the cloud. The problem to be decided is that which one of the available edge devices should be selected for unloading and then unloaded. This problem is classified as one of the hard non-polynomial problems and by using deterministic algorithms simply and in polynomial time, it is not possible to find a suitable and efficient solution for it found. Manuscript profile -
Open Access Article
18 - Reactive power compensation and reducing network transmission losses by optimal placement of parallel and series FACTS devices with fuzzy-evolutionary method.
Ali Motaghi Mohsen Alizadeh Mohammad Ali AbbasianThe growing use of energy in the world necessitates the development of power networks. However, developing new transmission lines requires a great deal of time and cost, so it will be very cost-effective to use the same lines with higher transmission capacities, if poss MoreThe growing use of energy in the world necessitates the development of power networks. However, developing new transmission lines requires a great deal of time and cost, so it will be very cost-effective to use the same lines with higher transmission capacities, if possible. In this regard, in recent years, by introducing of FACTS to power networks, their use in industrialized countries has become commonplace to increase the capacity of transmission lines. In this paper, the optimal adjustment of reactive power sources in the power network with FACTS series and parallel devices (TCSC, SVC) in order to coordinate them with each other and using fuzzy logic based on evolutionary algorithms such as particle swarm to reduce power losses Active, operating system costs including the cost of FACTS devices and congestion in transmission networks. Finally, this will be proven by simulating the IEEE 30-Bus test network and placing FACTS devices on it. Manuscript profile -
Open Access Article
19 - Reactive Power Optimization in the Presence of FACTS Devices Using Evolutionary Algorithms based on Fuzzy Logic
Sasan Ghasemi Eskandar GholipoorIn this paper to set the parameters of FACTS devices, genetics and particle swarm optimization with fuzzy logic techniques have been used. To optimize the reactive power consumption and reduce the line congestion, two types of FACTS devices; thyristor controlled series MoreIn this paper to set the parameters of FACTS devices, genetics and particle swarm optimization with fuzzy logic techniques have been used. To optimize the reactive power consumption and reduce the line congestion, two types of FACTS devices; thyristor controlled series compensator (TCSC) and static var compensator (SVC), are used. Optimal location of FACTS devices on the network, which is under heavy loads, results to reduce the power losses, reactive power control and thus reduces the operating costs of the power system. In this paper, the fuzzy membership functions are used in order to determine the weak network buses in order to install the SVC. The values of reactive power through the lines are leads to locate the line which the TCSC should be installed. The method presented in this paper have been compared with other methods (e.g. analysis of eigenvalues) for optimal location of FACTS devices. The results of the simulations presented in this paper, proves the efficiency of the proposed method. Manuscript profile -
Open Access Article
20 - Novel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem
Somayeh Ghanbari Rahil Hosseini Mahdi Mazinani -
Open Access Article
21 - Analog Circuit Complementary Optimization Based on Evolutionary Algorithms and Artificial Neural Network
Behzad Rajabi Farhad Razaghian -
Open Access Article
22 - Evolutionary Interval Type-2 Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction
Aref Safari Rahil Hosseini -
Open Access Article
23 - Providing a model for predicting stock prices using ultra-innovative neural networks
Seyyed Hosein Miralavi zahra pourzamaniDue to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and MoreDue to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and accuracy using hybrid models. nowadays various patters are used including statistical technique (discriminate analysis , logistic , analysis factors) and artificial intelligent techniques ( neural networks(NN) , decision trees , case based reasoning , genetic algorithm , rough sets , support vector machine , fuzzy logic ) and the combination of these two technique for predicating stock prices. For most predictive models, the system uses only one indicator to predict, but in the proposed model in this study, a two-level system of multilayered perceptron neural networks is presented which uses several indicators to predict. To do this, required information of Tehran Stock Exchange price indicators, for fiscal years 2012 - 2017 was collected. We also used the Grasshopper Optimization Algorithm to select the best samples for better nerve network training and thus to improve the results. The results show that the proposed model can operate with lower prediction error than other models. Manuscript profile -
Open Access Article
24 - Mining quantitative association rules with stock trading data using multi-objective Meta heuristic algorithms based on genetic algorithm
mostafa zandiyeh Sima MardanluForecasting stock return is an important financial subject that has attracted researchers’ attention for many years. Investors have been trying to find a way to predict stock prices and to find the right stocks and right timing to buy or sell. Recently, data minin MoreForecasting stock return is an important financial subject that has attracted researchers’ attention for many years. Investors have been trying to find a way to predict stock prices and to find the right stocks and right timing to buy or sell. Recently, data mining techniques and artificial intelligence techniques have been applied to this area. Association discovery is one of the most common Data Mining techniques used to extract interesting knowledge from large datasets. In this paper, we propose a new multi-objective evolutionary model which maximizes the omprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules from financial datasets, including 10 common indicators of technical analysis. To accomplish this, the model extends the two well-known Multi-objective Evolutionary Algorithms, Non-dominated Sorting Genetic Algorithm II and Non-dominated Ranked Genetic Algorithm, to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule. Moreover, this proposal introduces an external population and a restarting process to the evolutionary model in order to store all the nondominated rules found and improve the diversity of the rule set obtained. The results obtained over real-world stock datasets demonstrate the effectiveness of the proposed approach. Manuscript profile -
Open Access Article
25 - Multiobjective Imperialist Competitive Evolutionary Algorithm for Solving Nonlinear Constrained Programming Problems
chunan liu -
Open Access Article
26 - Optimal Recloser Placement by Binary Differential Evolutionary Algorithm to Improve Reliability of Distribution System
Maryam Falah nezhadnaeini Mohammad Hajivand Reihaneh Karimi Mohammad Karimi
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