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In the present paper, crop pattern criteria have been evaluated relying on sustainable development to increase agricultural water productivity. Seven criteria were selected as the main environmental and economic criteria and were prioritized and reviewed for important and strategic products in the Tajan catchment of Mazandaran province. Criteria prioritization was done using optimization through a genetic algorithm with an objective function based on sustainable development. Then, physical and economic productivity indices were calculated to determine the productivity value. Based on the results, in the selection of the crop pattern, firstly, the category of economic criteria and finally the category of environmental criteria have been given attention to the farmers in the current situation. But in the genetic optimization algorithm, all priorities have a similar order from the environmental point of view and then from the economic point of view although each product has its order of criteria. By this prioritization, the parameters of the cultivated area, the volume of water consumed, and the amount of chemical fertilizers have decreased on average by 26%, 34%, and 21%, respectively, and the parameters of product performance and profitability have increased by 43% and 61%, respectively. In addition to providing environmental standards and increasing sustainable development, this prioritization causes an average increase in physical productivity by 84% and an increase in economic productivity by 72%. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a flow shop scheduling problem for equipment overhaul. This problem consists of three stages, the separation of the components of an equipment is done in the first stage. Repairs and overhaul operations are carried out on the separated parts of the first stage in the second stage. Finally, the overhauled parts of the previous stage are mounted on each other in the third stage. In the third stage, operations are performed in parallel workshops. The objective function of the problem is the maximum time to complete jobs, and the sequence of processing jobs should be done in such a way that the value of the objective function is minimized. Design/methodology/approach: To solve the problem, a mixed integer programming model has been proposed for small size, which determines the processing sequence of jobs based on the position of each job. A genetic algorithm has been used to solve the problem in large dimensions. By increasing the size of the problem and in different sizes, the results have been examined and analyzed, which shows the efficiency of the model and the proposed algorithm. Findings: To check the accuracy of the model's performance and also the effect of the presence of parallel machines in the third stage, an example was presented in this paper. Accordingly, while the accuracy of the model's performance was checked, the effect of the presence of more machines was determined in the third stage. As the number of machines increased in the third stage, the value of the objective function did not deteriorate. The performance of GAMS in solving the problem in a small size was investigated. Considering that solving the problem for large dimensions is not possible in a reasonable time and the problem is NP-hard, then solving the problem in large dimensions was done using a genetic algorithm. Therefore, solving the problem on a large dimension has been done using the genetic algorithm. According to the obtained results, the efficiency of the genetic algorithm was shown. Due to its low average value, it indicated the convergence of the genetic algorithm. Research limitations/implications: Considering that there are not many published papers in the field of equipment overhaul, it is difficult to access related models and papers. Therefore, in this paper, the model and solution method have been written with many reviews. Also, to check and reduce costs, the number of third-stage machines has been determined using sensitivity analysis. Practical implications: The problem of equipment overhaul is used in many fields in reality. For example, the operations related to the maintenance, repair and overhaul of the aircraft engine have been investigated. Since the planning of maintenance and maintenance operations is difficult, the focus of research has been on improving maintenance operations by finding suitable scheduling for job shop operations in maintenance operations. They emphasized that scheduling can improve maintenance operations and presented a simulation model. Social implications: The purpose of creating a space to start an activity is to reduce costs, earn money and achieve profit. To examine the cost-effectiveness of the equipment overhaul issue, we can refer to the number of third-stage machines. According to the amount of equipment to perform an overhaul on them, the number of third-stage machines can be determined. Therefore, extra machines can be removed to reduce the cost. On the other hand, if the related equipment to the customers is different, to reduce the storage costs or increase customer satisfaction, different goals should be considered. Here, the objective function of maximum completion time is considered for this purpose. If the equipment must be available at a certain time, goals such as the total time to complete the job can be considered. In line with the application of the social implications in the investigated problem in this paper and considering that the investment costs, as well as the ability to respond to the applicants' requests, are related to the number of third-stage machines, the value of the objective function is analyzed based on the number of third-stage machines and analysis has been done. Originality/value: In this paper, a three-stage flow shop scheduling problem in the overhaul industry was studied. Accordingly, a new mathematical model based on the job processing position was proposed, which dealt with the exact solution of the problem in small dimensions. According to the type of problem in the overhaul environment, the combined flow shop problem for equipment overhaul was investigated. Also, the use of parallel machines in the third stage of the equipment overhaul problem is one of the new issues under investigation. [ABSTRACT FROM AUTHOR]

Surface runoff is one of the most significant components of the water cycle, which increases soil erosion and sediment transportation in rivers and decreases the water quality of rivers. Therefore, accurate prediction of hydrological response of watersheds is one of the important steps in regional planning and management plans. In this regard, the rainfall-runoff modeling helps hydrological researchers, especially in water engineering sciences. The present study was conducted to analyze the rainfall-runoff simulation in the Gorganrood watershed located in northeastern Iran using AWBM, Sacramento, SimHyd, SMAR, and Tank models. Daily rainfall, daily evapotranspiration, and daily runoff of seven hydrometric stations in the period of 1970-2010 and 2011-2015 were used for calibration and validation, respectively. The automated calibration process was performed using genetic evolutionary search algorithms and SCEUA methods, using Nash Sutcliffe Efficiency (NSE) and root mean of square error (RMSE) evaluation criteria. The results indicated that the SimHyd model with NSE of 0.66, TANK model using Genetic Algorithm and SCE-UA methods with NSE of 0.67 and 0.66, and Sacramento model using genetic algorithm and SCE-UA methods with NSE of 0.52 and 0.55 have the best performance in the validation period. [ABSTRACT FROM AUTHOR]

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments. [ABSTRACT FROM AUTHOR]

In recent years, the use of renewable energy systems has grown significantly, among which photovoltaic systems have received much attention. Solar cells are known as the building blocks of a photovoltaic system. Because of the nonlinear nature of solar cells and the continuous changes in atmospheric conditions, maximum power point tracking (MPPT) is essential to extract the maximum power of a photovoltaic system. In this study, in order to achieve the maximum power, it was proposed to apply flower pollination algorithm (FPA) combined with a comprehensive selection algorithm, named as improved FPA. In addition, to evaluate the proposed algorithm, its performance was compared with genetic algorithm (GA) and standard FPA under rapid changes in atmospheric conditions. The calculated results showed that the improved FPA has a better accuracy than GA; moreover it has a higher convergence rate as compared with other applied algorithms. [ABSTRACT FROM AUTHOR]

Long-term prediction of time series is an important but challenging issue. Today, deep networks, especially Long short Term Memory (LSTM) networks, have been successfully used to predict time series. The LSTM network is capable of maintaining long-term dependencies, but its ability to assign varying degrees of attention to sub window features over multiple time steps is not sufficient. Also, the performance of these networks depends heavily on their hyper-parameters and it is important to adopt an efficient method to ensure optimum values. In this study, to overcome the above challenges, an evolutionary attention-based deep LSTM for predicting multivariate time series is recommended that automatically finds one of the best combinations of LSTM parameter values and sub window features. The proposed algorithm uses a genetic algorithm to properly adjust the deep LSTM network architecture. In order to evaluate the performance of the proposed algorithm, three data sets in the fields of energy and environment have been used. The experimental results show that the proposed algorithm performs better than other basic models. [ABSTRACT FROM AUTHOR]

Permanent magnet machines are widely used in wind turbines due to theirs prominent characteristics. The direct-drive wind generators have large size and high manufacturing cost because of low operational speed so that these disadvantages deteriorate when the rated output power increases. Therefore, the main objective of the optimal design proposed in this paper is to minimize the wind turbine system cost maintaining the high efficiency characteristic. Thus, a multiphysics model of the wind generation system is firstly presented to be the basis of the optimal design. One of the paramount feature of the current study is to precisely calculate all of the energy production costs, including the manufacturing cost of generator, power converter, electrical subsystem, and also wind turbine tower cost. In order to obtain the optimum solution, the Genetic Algorithm (GA) optimization method is implemented with consideration of a predetermined proper range for optimal variables. By comparing the optimal design results with those attained by the innerrotor permanent magnet generator, the superiority of the proposed method is justified in terms of the total volume, weight, and annual output energy. Eventually, the finite element analysis has also been carried out to validate the outcomes obtained from the proposed optimal design. [ABSTRACT FROM AUTHOR]

Surge tanks and air chambers are the most useful solution to deal with water hammer in water transmission systems (WTS). The optimal design of these protective devices can be effective in reducing the costs of constructing and operating a water transmission system. In this article, some software with the capability of simulating and optimizing these protective equipment is presented. To simulate the behavior of the system in the transient condition, the characteristic method was used. To optimize the number, dimensions and location of the surge tanks and air chambers, the genetic algorithm was employed. Constraints of the problem included the control of negative and positive pressures within the permissible range to prevent the cavitation and water hammer. To test the performance of simulation and optimization models, a well-known water transmission system in the previous research was selected as a case study. The results indicated that the critical heads were damped to a safer and allowable range; also, the total cost of the surge tanks and air chambers was reduced by 17% by the proposed method. [ABSTRACT FROM AUTHOR]

Drying is one of the ways of storing of persimmon. In this study, to increasing shelf life of persimmon and producing high-quality products, infrared dryer was used and mass transfer kinetics, density, rehydration and color of samples were measured. The results showed that radiation lamp power and distance of lamp from sample had significant effect on the moisture loss kinetics and drying time (P<0.05). With increasing in radiation power, as well as reducing the distance of samples from the source of radiation, drying time decreased. The average density and rehydration for the dried samples in infrared were 639 kg /m3 and 270 %, respectively. The average calculated color changes (ΔE) for the power of 200, 300 and 400 w were 14.43, 10.09 and 20.04, respectively. The results of modeling by genetic algorithm-artificial neural network showed that artificial neural network combined with genetic algorithm provides better results and with combine them the speed of analysis and accuracy of modeling process increases. Using a network with 15 neurons in the hidden layer and using the hyperbolic tangent activation function and percentage data used to training/validation/testing equal 20/20/60 may be predicted drying kinetics of persimmon. [ABSTRACT FROM AUTHOR]

The growing use of internet and the existence of vulnerable points in networks have made the use of intrusion detection systems as one of the most important security elements. Intrusion detection is essentially a classification problem and it is the identification of effective features such as important issues in the classification This paper presents a novel method for selecting effective features in network intrusion detection based on an estimation of distribution algorithm that uses a probabilistic dependency tree to identify important interactions between features. To evaluate the performance of the proposed method, the NSL- KDD dataset is used, in which the packets are divided into five normal types and intrusive types of DOS, U2R, R2L and Prob. The performance of the proposed algorithm has been compared alone and in combination with other feature selection algorithms such as forward selection, backward selection and genetic algorithm. Moreover, the effect of algorithm parameters like population size on intrusion detection accuracy is tested. Based on this analysis and also considering the intra-class accuracy of different feature selection methods studied in this paper, an effective subset of features for intrusion detection is identified. [ABSTRACT FROM AUTHOR]

In this study, the discharge coefficient of the circular side orifices was predicted using a new hybrid method. Combinations made in this study were divided into two sections: 1) the combination of two algorithms including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and providing the PSOGA algorithm 2) using the PSOGA algorithm in order to optimize the Adaptive Neuro Fuzzy Inference Systems (ANFIS) network and providing the ANFIS-PSOGA method. Next, by identifying the parameters affecting on the discharge coefficient of the circular side orifices, 11 different combinations were provided. Then, the sensitivity analysis conducted by ANFIS showed that the Froude number and the ratio of the flow depth to the orifice diameter (Ym/D) were identified as the most effective parameters in modeling the discharge coefficient. Also, the best combination including the Froude number (Fr), the ratio of the main channel width to the side orifice diameter (B/D), the ratio of the orifice crest height to its diameter (W/D) and the ratio of the flow depth to the orifice diameter (Ym/D) for estimating the discharge coefficient was introduced. For this model, the values of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and correlation coefficient (R) were obtained 0.021, 0.020 and 0.871, respectively. Additionally, the performance of the ANFIS-PSOGA method was compared with the ANFIS-PSO and ANFIS methods. The results showed that the ANFISPSOGA method for predicting the discharge coefficient was the superior model [ABSTRACT FROM AUTHOR]

Estimation of discharge as one of the major issues in water resource management and flood control has a key role in the success of water construction design and efficiency of Bio-Mechanical proceeding in catchments. In this research, discharge Peak of Yalphan River has been simulated using three main methods of artificial intelligence (MLP neural network model, subtractive clustering and ANFIS model, and the combination of neural network and genetic algorithm). For this purpose, 8 parameters have been prepared as input data (2001-2012) including precipitation in the event day, precipitations during 5 days before the event day, base flow in the event day and CN map. Peak of flow has been considered as output data of models. RSME, MAE and NSE indicators has been used to assess the artificial intelligence models. Output data of neural network model have been imported to the combined model of neural network and genetic algorithm. Also, output data of subtractive clustering model have been imported to ANFIS model. Finally three models have been assessed using the mentioned indicators. The results showed that the combined model of neural network and genetic algorithm is better than the other models in Yalphan Catchment. [ABSTRACT FROM AUTHOR]

Nowadays distributed generation has been given considerable attention due to the increase in energy consumption and the need for using renewable energies. So, the concept of the micro grids for connecting and coordinating different distributed resources via the local power control systems has been introduced. A micro grid connects to the network at normal condition of the operation but in order to increase the system reliability and to promote the generated power quality to feed the sensitive loads, they can operate in the islanding mode too. In the islanding mode of operation in order to supply the whole load and to maintain the balance between the supply and demand, these resources use the conventional droop methods. Generally, using the conventional voltage droop control due to the difference in the impedance of the feeder connect to the resources and also the asymmetrical nature of the network, dose not lead to an appropriate reactive power sharing between the resources. In fact, using conventional droop control creates an intrinsic compromise between the accuracy of reactive power sharing and voltage regulation of micro grid buses. Therefore, in the proposed method of this paper, the coefficients of the voltage droops characteristics of the grid resources are determined by solving an optimization problem using the genetic algorithm (GA). Also in order to improve the reactive power sharing, a novel control strategy based on the adaptive virtual impedance is presented. In this method the ideal reactive power sharing is achieved by using the virtual impedance and compensating the voltage drop of the lines. The virtual impedance setting is done through the data of the voltage of PCC bus in microgrid and sending via a low bandwidth communicational link to the local controller of each resource. The proposed method is robust against the network structure changes, presence of local loads, and interruption in the communication link. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a bi-objective integrated of production distribution planning problem model in a multi echelon supply chain is presented. This model is considered in a four echelon supply chain includes suppliers, manufactures, distribution centers and customers with several types of raw material and product in multi-time period. In addition to minimize the total cost of supply chain including transportation and supply of materials costs, preparation and production of products, inventory holding of materials and products in manufacture and distribution centers, transportation and purchase of products for distributors and customers, and backorder cost, the second objective is to decrease the transfer time of products in order to increasing the service level for customers. Two Pareto-based multi-objective metaheuristic algorithms namely non-dominated sorting genetic algorithm (NSGA-II) and nondominated ranking genetic algorithm (NRGA) are proposed to solve the model. Since the solution quality of all meta-heuristic algorithms severely depended on their parameters, Taghuchi method has been utilized to tune the parameters of algorithms. Finally, computational results obtained by implementing the algorithms on several problems of different sizes demonstrate the performance of proposed methodologies. [ABSTRACT FROM AUTHOR]

Introduction: Early detection of coronary artery disease is critical. Previous studies show that new methods of artificial intelligence for prediction and diagnosis of diseases have been widely considered by the researchers. The purpose of this paper is the classification of angiography to normal and abnormal by using Artificial Intelligent methods. Methods: In this diagnostic study, the datasets were collected from 152 patients who had been undergoing the coronary angiography, and for classification of the angiography results, a combination of fuzzy neural network and genetic algorithm is used. The proposed system was implemented and evaluated by MATLAB software. Results: In implementation of the proposed system, 85% of the data was used for the training phase and the remaining 15 percent was used for the test phase. The results of the simulation in accuracy, sensitivity and specificity indicators on average state were 0.9496, 0.9253, 0.9435 and 0.9569, respectively. Under optimum conditions, an estimate of 1 was obtained for each indicator. Conclusion: The use of genetic algorithms in ANFIS training phase led to improved speed of the simulation. Also the high performance indicators prove that the proposed system is effective for classifying and diagnosing patients with coronary heart disease. [ABSTRACT FROM AUTHOR]

In recent years, the relevant subject to robotic sciences has been converted to one of progressive research field. Among them intelligent robots have been accepted very well, but the control and navigation of these instruments have been very hard and obstacles avoidance in order to find a safe navigation is essential for these systems. A mobile robot should reach to the definitive targets in a certain time and the parameters such as speed, safety and environment recognition, etc. could be improved along the way. Robot must determine its destination to the targets location by each step to be able to control the strategy for reaching to these targets. In addition information gain about the environment is also needed for obstacles avoidance, planning for the navigation and environment identification. Besides an intelligent relationship between senses and in necessary to gain from suitable algorithms such as genetic Algorithm and fuzzy system (Fuzzy Controller) for control and navigation. Since the real environments are rarely predictable, simulating a main rout before starting in not applicable in general. Fuzzy logic method however should be more applicable in routing moment and node to node. Therefor in this proposal the optimized methods in fuzzy-genetic systems (The conditions are changing and obstacles are moving), are selected to solve the navigation problem of mobile robot in dynamic environment. Using these algorithms (Fuzzy-Genetic), the mobile robot is able to move in different environment and particularly in dynamic and unknown environment . [ABSTRACT FROM AUTHOR]

Production planning is one of the most important challenges in steel industry. Order planning is one major part of this aspect according to the fact that production is usually planned based on customer orders in this industry. Inventory planning is also vital for maximizing capacity utilization. The proposed model in this paper is capable of selecting order, assigning finish time of each order, and determining open orders in each process by considering order planning and inventory planning simultaneously. The model could have a key role in improving production planning of steel companies by on time delivering of the orders, reducing work-in-process, and balancing utility of capacities. Finally, a heuristic algorithm based on genetic algorithm is proposed and its efficiency evaluated in an empirical case study of Iran alloy steel enterprise. [ABSTRACT FROM AUTHOR]

Today, with advances in use of intelligent systems technology for data analysis, processing in the diagnosis of various diseases is of particular importance. In this paper, an intelligent method that combines xcs and LA are offered to diagnose heart disease. Classification system (XCS) which is considered as one of the most successful agents in learning to solve problems (Learning Agents) is known, rules based on its accuracy in predicting the reward received from the environment, evaluating and using genetic algorithms to update. In this paper, using data from the UCI database, the proposed method deals with the education system And then use the test data to evaluate system performance. the Improve efficiency of the method compared to previous methods for detecting heart disease show. [ABSTRACT FROM AUTHOR]

In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective of this chain is maximizing the total profit. The second objective is minimizing the use of raw materials. In the other words, the second objective is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and Epsilon-constraint method is used for finding the pareto optimal line,. Finally, a numerical example is solved with proposed approach and performance of the model is evaluated for different sizes. The results show that this approach is effective and useful for managerial decisions. [ABSTRACT FROM AUTHOR]