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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

Increase of nonlinear loads in industries has resulted in high levels of harmonic currents and consequently harmonic voltages in power networks. Harmonics have several negative effects such as higher energy losses and equipment life reduction. To reduce the levels of harmonics in power networks, different methods of harmonic suppression have been employed. The basic idea in all of these methods is to prevent harmonics from flowing into a power network at customer sides and the point of common coupling (PCC). Due to the costs, none of the existing mitigating methods result in a harmonic-free power system. The remaining harmonic currents, which rotate in a power network according to the system impedances, may not necessarily result in an optimum harmonic power flow in terms of harmonic undesirable results such as harmonic losses or harmonic over voltage/current due to possible resonances. This paper proposes a new method to control the flow of the remaining harmonics at a power system level such that the effects of harmonics such as losses are optimized. This task is achieved by the use of series active power filters controlled in a different manner from their conventional methods. In this new application, the series active power injects a controlled harmonic voltage into a power system (e.g., a line, transformer, or other elements) to control the harmonic current (power) flow such that the overall performance of the power system is improved in terms of harmonic effects. The series active power filter in this new application is named as harmonic power flow controller (HPFC). In this paper, the theory, structure, applications, and the basics of the control method of a HPFC are described. To investigate the effects of the HPFC on the power network harmonics, an HPFC is used in the 14-busbar IEEE test system. To make the proposal more practical, an HPFC is designed for Iran north-west transmission system to alleviate the harmonic problem in this region. Simulations are carried out to show the effectiveness of the HPFC. Simulation results show that the HPFC can control the harmonic currents (power) and voltage consequently. In this paper, several control algorithms for an HPFC are also considered to achieve the desired harmonic voltages levels. [ABSTRACT FROM AUTHOR]

Automation has attracted interest from the industry sector for its potential to improve energy efficiency, cost efficiency, and environmental performance. By elevating the LoA to the highest degree, associated costs will grow accordingly and its implementation will be far more complicated. This will also result in losing workers and decreasing environmental pollutants. On the other hand, increasing power consumption at high levels of automation leads to the production of greenhouse gases. This paper aims to increase the level of automation (LoA) considering the concept of sustainability. This study presents fuzzy multi-objective programming to determine the optimal LoA considering sustainability factors to achieve competitive advantages. To solve the model, the Zimmermann max-min approach was adopted and a cosmetics factory in Iran was chosen to optimize LoA according to this model. The results showed that it is possible to improve the LoA and also consider sustainability factors with the available resources without using the highest LoA. This study can help managers optimize the LoA in their organizations considering the current resources and sustainability issues, and control the company's return on investment and cost of overhead. They can run the model with every definition of LoA proposed till now. This research can benefit the environment and the workers' health in the production line by reducing environmental pollutants and prevent the dismissal of all personnel due to its negative social effects. It also reduces the risk of COVID-19 by minimizing the number of workers. So far, a mathematical model for selecting optimal LoA in the chemical industry considering sustainability has not been presented. [ABSTRACT FROM AUTHOR]

In this paper, the optimization algorithm based on the population as improved global particle swarm optimization is described and used for inverse modelling of two-dimensional magnetic field data. This algorithm is able to estimate the parameters of depth, shape factor, amplitude coefficient, magnetic inclination angle and origin point coordinates. To evaluate the efficiency of this method, the magnetic field of an artificial model was analysed, with and without added random noise. The results suggest that the proposed algorithm is capable of model parameter estimation with high accuracy. Accordingly, the improved global particle swarm optimization algorithm was used to analyse the magnetic field of the study area in the Ileh region in Iran located in Taybad city. The study area is very rich in terms of iron resources. The estimate for the study area is that the depth of the buried mass centre is about 114.9 m and its approximate shape is similar to a horizontal cylinder based on the calculated shape factor value which is 1.76. The calculated depth is an acceptable match with the average depth of drillings. [ABSTRACT FROM AUTHOR]

This paper develops and applies algorithms for optimally merging satellite precipitation products with rain-gauge precipitation for accurate rainfall estimation. The satellite-based precipitation products (SBPs) PERSIANN-CDR, TMPA-3B42, GPM-IMERG, and GSMaP MKV are combined and evaluated to generate accurate rainfall estimates. Daily satellite precipitation data are compared with corresponding station data to calculate the bias for the period 2014–2019. Three different algorithms are proposed whose adjustable parameters are optimally determined by solving constrained optimization algorithms to produce combinations of satellite-based precipitation products. The optimal combination is named optimally merged satellite-based precipitation (OMSBP). The root mean square error (RMSE), coefficient correlation (CC), and the Nash–Sutcliffe error (NSE) are employed to test the proposed method with precipitation data for the Tehran urban region, Iran. The spatially resolved results over the studied urban area establish that TMPA-3B42, with an average value MAE, MBE, and RMSE equal to 0.68 mm, − 0.31 mm, and 2.94 mm, leads to better estimates of precipitation than those of PERSIANN-CDR, IMERG, and GSMaP MKV. Moreover, algorithms alg7 and alg8 yielded better results with respect to the MAE and MBE, respectively. Lastly, algorithm alg3 produced better results than alg7 and alg8 based on the RMSE, NSE, and CC corresponding to precipitation predictions. [ABSTRACT FROM AUTHOR]

In this paper, exergy analysis of a steam power plant located in southern Iran named Zarand power plant has been studied. In order to optimize the performance of the Rankine cycle and achieve higher exergy efficiency, several parameters have been considered as decision variables. Knowing that there is the ability to change some of the parameters in the specific range in the process of electricity production in power plant, temperature and output pressure of the boiler and output pressure of four steps of extraction turbine have been selected as six decision variables. Also, exergy efficiency has been considered as the objective function. For this purpose, the exergy efficiency of the system is optimized using intelligent algorithms including bees, fireflies, and algorithm based on teaching and learning and they are compared with each other. The results show that in the case of suitable changes of decision variables and applying appropriate algorithms, exergy efficiency of the studied thermal power plant can be increased from 30.1% to 30.68047%, 30.70368%, and 30.70369%, respectively. It means using optimization algorithms of bees, fireflies, training-learning, exergy efficiency of the Rankine cycle of the studied power plant can be increased by 0.58047%, 0.60368%, and 0.60369%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a new thermodynamic cycle for power and fresh water production is presented, which includes MED-TVC desalinations and Allam cycles due to the important influence they have on the electric power industry in controlling and limiting greenhouse emissions. First, the Allam cycle analyzed and the appropriate location for heat-extraction identified to produce the required water vapor for the desalination system. It was determined that the heat site suitable for use in the desalination cycle is the heat from the outlet of the Compressor, which is estimated as 100 MW. The MED-TVC Desalination, one of the most suitable and most economical desalination, is used to combine the Allam cycle and desalination in the Kish district, located in the south of Iran. The proposed cycle is analyzed from the perspective of energy and exergy. The results show, the highest amount of exergy used to generate power in turbine and the amount of produced freshwater increase with increasing the capacity of Turbine. For this purpose genetic algorithm is used in two different scenarios to minimize and maximize the exergy destruction and produced fresh water respectively. The optimization of the system with genetic algorithm led to 18% decrease in total exergy destruction of the cycle in the first scenario and 7% decrease in the second optimization scenario. Furthermore, the efficiency of the cycle in first scenario and second scenario increased by 30% and 13% respectively. In the scenario optimization scheme, the amount of fresh water increased by 22%. [ABSTRACT FROM AUTHOR]

In this paper, a wind/photovoltaic/battery/diesel hybrid system with hourly analysis during a year is modeled and optimized for different cities of Iran with various ranges of wind, solar and ambient temperature. A number of solar panels, wind turbines, batteries as well as nominal capacity of diesel engine are considered as design parameters. Fuel ratio and total annual cost (TAC) are selected as two simultaneous objective functions, and particle swarm optimization algorithm is used to find the optimum value of design parameter. The optimum results reveal that Zahedan has the significant lower TAC compared with other studied cases. Optimum cost increases 4.63%, 5.85%, 8.24%, 15.45% and 17.60%, respectively, in the Bushehr, Kerman, Tabriz, Tehran and Mashhad as compared with Zahedan. Optimum results reveal that annual cost extremely increases for the system with the absence of diesel engine (highest fuel ratio). For example, TAC increases at least 100% for the improvement of fuel ratio from 0.8 to 1 for all studied cities. The results also reveal a regular fluctuation in the battery charging and discharging is observed in the high available radiation cities such as Zahedan and Kerman. Furthermore, TAC increases in a linear manner, while fuel ratio increases in an exponentially manner with the increase in the number of PV panels in the optimum situation for all studied cases. [ABSTRACT FROM AUTHOR]

The special features of some old residential complexes make reconstructing a better option than rebuilding. The complexity of redesign issues, the ability of computers to solve problems, and the capability of evolutionary optimization techniques in architecture make the use of these methods a priority over traditional processes. This paper, while introducing Particle Swarm Optimization (PSO) as one of the swarm intelligence based algorithms, answers the question of how this algorithm can be used to redesign residential complexes. To achieve this, after reviewing the application of PSO algorithms in architectural space planning, the redesign criteria and constraints was divided into four categories; including primary design specifications, contact-related criteria, designer-related criteria and redesign-related constraints. After the quantification process of these criteria, how to implement the algorithm to achieve optimal plans for each of the five plan types available in high-rise apartment complex in Mashhad, Iran, was shown as a case study. [ABSTRACT FROM AUTHOR]

Tunnel construction in urban areas causes ground displacement which may distort and damage overlying buildings and municipal utilities. It is therefore extremely important to predict tunneling-induced ground movements in tunneling projects. To predict the tunneling-induced ground movements, artificial neural networks (ANNs) have been used as flexible non-linear approximation functions. These methods, however, have significant limitations that decrease their accuracy and applicability. To overcome these problems, the use of optimization algorithms to train ANNs is of advantage. In this paper, a hybrid particle swarm optimization (PSO) algorithm-based ANN is developed to predict the maximum surface settlement and inflection points in transverse and longitudinal directions. Subsequently, the transverse and longitudinal troughs were obtained by means of empirical equations and 3D surface settlement troughs were ploted. For this purpose, extensive data consisting of measured settlements from 123 settlement markers, geotechnical properties and tunneling parameters were collected from the Karaj Urban Railway Project in Iran. The optimum values of PSO parameters were determined with the help of sensitivity analysis. On the other hand, to find the optimal architecture of the network, trial-and-error method was used. The final hybrid model including eight inputs, a hidden layer and three outputs was used to predict transverse and longitudinal tunneling-induced ground movements. The results demonstrated that the proposed model can very accurately predict three-dimensional ground movements induced by tunneling. [ABSTRACT FROM AUTHOR]

This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved. [ABSTRACT FROM AUTHOR]

Flight network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the air transport network is analyzed with a multi-objective genetic algorithm to reduce the number of airways and to aggregate the passengers and also to reduce route changes and the travel time fortravelers. The proposed topology model of this study is based on the combination of two topologies – point-to-point and hub-and-spoke – with multiple goals for decreasing in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. Four state-of-the-art Multi-objective Genetic Algorithms (MOGAs) are considered for comparison studies and are tested and assessed in data of the Iran airline industry in 2018, as an experiment to real-world applications. Using the combination of point-to-point and hub-and-spoke topologies can improve the performance of the MOGA to solve a network-wide flight trajectory planning. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 km) and up to 18%, respectively. The proposed model also suggests that the current air routes of Iran can be decreased to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 km) and 5%, respectively. The simulation results show the potential benefits of the proposed model and the advantages of it. Optimizing the structure of the flight network can significantly reduce operational cost while ensuring the operation safety. According to the results, the multi-objective optimization model is successfully able to precisely design the efficiently optimized airline topologies. [ABSTRACT FROM AUTHOR]

This paper addresses a possible decrease in flood damages by using structural methods and determines proper locations (layout) to construct protective levees and height of levees (design of levees) in high-risk areas. Because of discontinuity of the objective function and a large number of decision variables, the optimization uses the genetic algorithm (GA). The simulation of the region is carried out with the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model. The GA is employed to maximize the benefit of flood control and also to minimize the cost of protective levees' construction. Thus, the fitness function of the algorithm maximizes the net benefit of the project. After a sensitivity analysis, mutation and crossover probability are assumed to be 0.03 and 0.7, respectively. Thus, 1,000 generations and 98 chromosomes for each run are considered. The optimization model is run 5 times for floods with return periods of 50 and 100 years. Furthermore, the algorithm is used to evaluate flood damages in the Sarm and Khoor Abad River, located in the Qom province of Iran. Project net benefits resulting from the optimization model for rivers using 100-year flood exceed those for a 50-year flood. Results indicate that construction of protective levees reduces the rate of damages up to 99% in comparison with a nonconstruction of levees scenario. By taking into account the construction cost of levees, net benefits decrease by 0.97% and 1.1% for floods with 50-year and 100-year return periods, respectively. [ABSTRACT FROM AUTHOR]

In recent years, research has shown a growing interest in the use of hybrid wind photovoltaic (PV) systems that provide better performance compared to the use of a single component due to complementarity in meeting electricity demand. Over the past twenty-five years, hundreds of articles have addressed the topic of hybrid systems considering different configurations and final uses and, over the past decades, many reviews have made a comprehensive summary of various results obtained. However, some reviews deal with the research in a too general and qualitative way, without providing quantitative data, and other reviews are too focused on a specific topic aspect. To provide a qualitative-quantitative prospect of the research trend in the last twenty-five years, the present work is aimed at carrying out a literature review and statistical analysis starting from data extracted from the 550 most relevant and recent articles concerning hybrid systems, published between 1995 and 2020. The review aim was to produce an upgradable matrix literature database that schematizes the content of all articles in terms of different categories, such as the geographical distribution, their component configurations, operating mode and the auxiliary components used to support it, their intended uses and study methodologies (simulation, experimental, economic, energy, environmental and social analysis etc.) and software used. In addition, all the optimization algorithm, energy, economic, environmental and social indicators available in the literature were extracted and elaborated to identify the most used. The 550 articles were analysed, compared, and classified into several categories to provide an overall framework of the state of the art. The objective is to clearly and appropriately show important trends and findings in the development of hybrid wind and solar PV experimental, simulation and optimization projects. Data are elaborated to obtain a statistical analysis for each category or a combination of categories. In particular, the analysis highlighted that research is more focused on testing systems in warm or temperate localities, with the Köppen climate groups B and C prevalent over the others. From the geographical point of view, Asia is the continent most involved in world research (with China, India and Iran the first three countries for total publications produced). However, also in other parts of the world, a growing interest was noticed in this technology. The prevalent tested system configuration mode is the stand-alone hybrid systems, in a wide variety of climates and especially for residential uses. Simulations are mostly implemented in the analysed publications, mainly through HOMER and MATLAB software. Parametric analysis is widely used for optimal system design with a large variety of techniques. In particular, the system performance is examined mostly from an energy point of view. Economic analysis is also very common, alone or in combination with energy analysis. The most frequently used optimization algorithms are the particle swarm optimization (PSO) and genetic algorithm (GA), while the loss of power supply probability (LPSP) and renewable fraction (RE) for the energy analysis, the net present cost (NPC) and cost of energy (COE) for the economic analysis and the emissions (E) of CO 2 for the environmental analysis are most widespread indicators. Finally, an analysis on the size of the system components is performed to study which renewable source is more preferred at low and high installed power, for stand-alone, grid-connected systems and overall, considering different intended uses. The analysis highlights that PV systems are preferred at low installed powers, especially for residential use and stand-alone mode, while wind systems, in addition to being extensively used for low installed powers, demonstrates higher employment compared to PV systems as the power increases. The paper findings and upgradable matrix literature database are proposed as a valuable tool for engineers, experts and national and international policymakers. • 550 relevant papers were reviewed and used to create an upgradable matrix literature database. • Asia is the continent most involved in world research with Iran, India, and China. • The dry and temperate climate zones of Köppen climate group BWh are the most investigated. • Stand-alone hybrid systems and residential use are the most explored applications. • The use of photovoltaic system is preferred for low installed powers, while wind systems also for high installed powers. [ABSTRACT FROM AUTHOR]

Identifying railway capacity is an important task that can identify ‘in principal’ whether the network can handle an intended traffic flow, and whether there is any free capacity left for additional train services. Capacity determination techniques can also be used to identify how best to improve an existing network, and at least cost. In this article, an optimization approach has been applied to a case study of the Iran national railway, in order to identify its current capacity and to optimally expand it given a variety of technical conditions. This railway is very important in Iran and will be upgraded extensively in the coming years. Hence, the conclusions in this article may help in that endeavor. A sensitivity analysis is recommended to evaluate a wider range of possible scenarios. Hence, more useful lower and upper bounds can be provided for the performance of the system. [ABSTRACT FROM AUTHOR]

One of the attractive issues in Data Envelopment Analysis (DEA) literature is to find the anchor points of the production possibility set (PPS). Each extreme efficient unit which is located on the intersection of the strong and weak efficient frontiers of the PPS, is called an anchor point. In the other word, a decision making unit (DMU) is an anchor point, if there is at least one supporting hyperplane at the unit under consideration, in the situation that some components of its gradient vector are equal to zero, and so some input or output factors do not play any role in the performance of that unit. This study presents a new method to identify the anchor points of the PPS under the variable returns to scale (VRS) assumption and in the presence of the uncertain data. The proposed method is based on the robust optimization technique and finding the weak and strong defining supporting hyperplanes passing through the unit under evaluation. The potentially of the proposed method is illustrated by a data set, includes 20 banks in Iran. [ABSTRACT FROM AUTHOR]

The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO), and eight different objective functions (R², bR², NSE, MNS, RSR, SSQR, KGE, and PBIAS) in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are "conditioned" on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area. [ABSTRACT FROM AUTHOR]

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models. [ABSTRACT FROM AUTHOR]

Regarding evaluating disaster risks in Iran's West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP's accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP. [ABSTRACT FROM AUTHOR]

Predicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomatoes are produced annually around the world. In this study, tomato yield based on data of 40 cities of Iran country including annual average temperature (T), relative humidity (RH), effective rainfall (R), wind speed (WS), and Evapotranspiration (EV) for the period of 1968–2018 was predicted using a new Bayesian model averaging (BMA). The paper's main innovation is the use of the new BMA so that it allows the modellers to quantify the uncertainty of model parameters and inputs simultaneously. For this aim, first, the multiple Adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) were used for predicting crop yield. To train the ANFIS and MLP model, a new algorithm, namely, multi verse optimization algorithm (MOA) was used. Also, the ability of MOA was benchmarked against the particle swarm optimization (PSO), and firefly algorithm (FFA). In the next level, the new BMA used the outputs of the ANFIS-MOA, MLP-MOA, ANFIS, FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP for predicting tomato yield in an ensemble framework. The five- input combination of RH, T, and R, WS, and EV gave the best result. The mean absolute error (MAE) of the BMA in the testing level was 20.12 (Ton/ha) while it was 24.12, 24.45, 24.67, 25.12, 29.12, 30.12, 31.12, and 33.45 for the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models. Regarding the results of uncertainty analysis, the uncertainty of BMA was lower than those of the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models while the MLP model provided the highest uncertainty. The results of this study indicated that BMA using multiple MLP and ANFIS model was useful for predicting tomato yield. [ABSTRACT FROM AUTHOR]

The Angstrom–Prescott (A–P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The aim of this research was calibration and validation of the coefficients of the A–P model at six meteorological stations across arid and semi-arid regions of Iran. This model has improved by adding the air temperature and relative humidity terms. Besides, the coefficients of the A–P model and improved models have calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error, and coefficient of determination (R2) have used to analyze the models ability in estimating Rs. The results indicated that the performance of the A–P model had more precision and less error than improved models in all the stations. In addition, the best results have obtained for the A–P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m−2 day−1 for the A–P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively. [ABSTRACT FROM AUTHOR]

Absorption chillers are one of the most used equipment in industrial, commercial, and domestic applications. For the places where high cooling is required, they are utilized in a network to perform the cooling demand. The main objective of the current study was to find the optimum operating conditions of a network of steam absorption chillers according to energy and economic viewpoints. Firstly, energy and economic analysis and modelling of the absorption chiller network were carried out to have a deep understanding of the network and investigate the effects of operating conditions. Finally, the particle swarm optimization search algorithm was employed to find an optimum levelized total costs of the plant. The absorption chiller network plant of the Marun Petrochemical Complex in Iran was selected as a case study. To verify the simulation results, the outputs of energy modelling were compared with the measured values. The comparison with experimental results indicated that the developed model could predict the working condition of the absorption chiller network with high accuracy. The economic analysis results revealed that the levelized total costs of the plant is 1730 $/kW and the pay-back period is three years. The optimization findings indicated that working at optimal conditions reduces the levelized total costs of the plant by 8.5%, compared to the design condition. [ABSTRACT FROM AUTHOR]

Stand-alone hybrid energy systems based on solar and energy storage are an effective option for rural areas to meet the load demand. The objective of the current work is to the optimal configuration of a stand-alone hybrid photovoltaic/battery energy storage system with the help of an efficient metaheuristic algorithm, improved harmony search, to supply electrical of a residential load in Iran. The objective function is a minimization of total life cycle cost (TLCC) subject to the reliability index (loss of load probability). The optimal configurations of hybrid systems are compared in respect of different losses of load probability (0 to 20%). Sensitivity analysis and effects of economic parameters based on photovoltaic and battery prices are carried out to study the possibility of the suggested scheme. The results show that, by increasing the reliability index from 0 to 20%, the optimal number of panels and batteries decreases by 52 and 1202. Also, it is found that the TLCC of the system and cost of system components are increased by decreasing of the reliability index value. [ABSTRACT FROM AUTHOR]

Renewable energy technologies have been developed in recent years due to the limited sources of fossil fuels, the possibility of depletion of fossil fuels and the related environmental issues. In these types of systems, it is crucial to reach optimum sizing in order to have an affordable system based on solar and wind energy and energy storage. In this study, a powerful optimization scheme based on tabu search, called discrete tabu search, has been proposed for sizing three stand-alone solar/wind/energy storage (battery) hybrid systems. For validating the applied algorithm effectiveness, the results are compared with the results found by the discrete harmony search. The obtained outcomes are compared on the basis of total annual cost. The components of the scheme are analyzed in different operating conditions by applying meteorological data in addition to real time information from three typical regions of Iran. According to the obtained data, applying 'discrete tabu search' leads to better outputs on the basis of mean, standard deviation and worst indexes. [ABSTRACT FROM AUTHOR]

Optimizing purposes of the drilling process include reduction in time, saving costs, and increasing efficiency, which requires optimization of controllable variables and variables affecting the drilling process. Drilling optimization is directly related to maximizing the rate of penetration (ROP). However, estimation of ROP is difficult due to the complexity of the relationship between the variables affecting the drilling process. The main goal of this study is to develop three computational intelligence (CI)-based models including multilayer perceptron neural network optimized by backpropagation algorithm (BP-MLPNN), cascade-forward neural network optimized by backpropagation algorithm, and radial basis function neural network optimized by biogeography-based optimization algorithm (BBO-RBFNN) to estimate ROP. Also, in order to broaden the comparisons, some conventional ROP models from the literature were employed. The required data were collected from the well log unit and the final drilling reports of four drilled wells in two different oil fields in southwestern Iran. Firstly, all data were preprocessed to remove outliers; then the overall noises of the data were reduced by implementing Savitzky–Golay smoothing filter. In the next stage, nine input variables were selected during a feature selection step by combining the BP-MLPNN and NSGA-II algorithm. The results of this study showed that developed CI-based models more accurate than conventional ROP models. Also, a survey of statistical indices and graphical error tools proved that BBO-RBFNN model has the highest performance to predict ROP with values of APRE, AAPRE, RMSE and R2 equal to − 0.603, 5.531, 0.490 and 0.948, respectively. [ABSTRACT FROM AUTHOR]

This paper brings together the benefits of a membrane-thermal desalination plant with an integrated system containing parabolic trough collectors and wind turbines in collaboration with the electrical network to produce power and freshwater. This proposed cogeneration system was employed for providing power and freshwater in Chabahar in Iran by three types of desalination system consist of the Reverse Osmosis (RO), Multi-effect distillation (MED), and Thermal Vapor Compression (TVC). Through this novel system, the heat generated in the parabolic trough collector is transferred into the organic Rankine cycle (ORC) to produce power. So, the generated power of the organic Rankine cycle and an auxiliary wind turbine can employ by the Reverse Osmosis unit, and the surplus amount of the electricity can sell to the electrical network. This configuration is enabled to increase the stability of the system, and it ultimately reduces the cost of water production by 23%. Besides, the integration of Multi-effect distillation (MED) and Thermal Vapor Compression (TVC) systems enjoys the rejected waste heat from the condenser to increase the produced freshwater. The obtained results from exergy analysis demonstrated that the exergy destruction of the solar collectors and wind turbines contributed by 39.5% and 22.2%, respectively. The results of multi-objective particle swarm optimization reveal that the exergy efficiency and the cost of freshwater production reach to 26.2% and 3.08 US$/m3, each. The environmental assessments of this system demonstrate that this hybrid system prevents the amount of 52164 tons of CO 2 emission per year. [ABSTRACT FROM AUTHOR]

Background: Growing demand for medical services has increased patient waiting time due to the limited number or unbalanced distribution of healthcare centers. Healthcare teleconsultation networks are one of the potentially powerful systems to overcome this problem. Medical pathology can hugely benefit from teleconsultation networks because having second opinions is precious for many cases; however, resource planning (i.e., assignment and distribution of pathology consultation requests) is challenging due to bulky medical images of patients. This results in high setup and operational costs. The aim of this study is to design an optimal teleconsultation network for pathology labs under the supervision of medical sciences universities in Tehran, Iran.Methods: To avoid the setup cost, we first propose a modified hybrid peer-to-peer (P2P) overlay architecture for our telepathology network, using Iran's National Healthcare Information Network (SHAMS) as the underlying infrastructure. Then we apply optimization techniques to solve the request assignment and distribution problems in the network. Finally, we present a novel mathematical model with the objective of minimizing the variable operational costs of the system.Results: The efficiency of the proposed method was evaluated by a set of practical-sized network instances simulated based on the characteristics of SHAMS. The results show that the presented model and architecture can obtain optimal solutions for network instances up to 350 nodes, which covers our target network.Conclusions: We believe that the proposed method can be beneficial for designing large-scale medical teleconsultation networks by adjusting the constraints according to the rules and conditions of each country. Our findings showed that teleconsultation networks in countries with strong information technology (IT) infrastructures are under the influence of consultation fees, while in countries with weak IT infrastructure, the transmission costs are more critical. To the best of our knowledge, no research has so far addressed resource planning in medical teleconsultation networks using optimization techniques. Besides, the target network, i.e., pathology labs under the supervision of medical sciences universities in Tehran and the SHAMS network, are discussed for the first time in this work. [ABSTRACT FROM AUTHOR]

Successful implementation of construction projects worldwide calls for a set of effective risk management plans in which uncertainties associated with risks and effective response strategies are addressed meticulously. Thus, this study aims to provide an optimization approach with which risk response strategies that maximize the utility function are selected. This selection is by opting for the most appropriate strategies with the highest impact on the project regarding the weight of each risk and budget constraints. Moreover, the risk assessment and response strategy of a construction project in Iran as a case study, based on the global standard of the project management body of knowledge (PMBOK) and related literature, is evaluated. To handle the complexity of the proposed model, different state of the art metaheuristic algorithms including the ant lion optimizer (ALO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), salp swarm algorithm (SSA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO). These algorithms are validated by the exact solver from CPLEX software and compare with each other. One finding from this comparison is the high performance of MFO and HHO algorithms. Based on some sensitivity analyses, an extensive discussion is provided to suggest managerial insights for real-world construction projects. [ABSTRACT FROM AUTHOR]