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Purpose: A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures. Design/methodology/approach: As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods. Findings: The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software. Originality/value: The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time. [ABSTRACT FROM AUTHOR]

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]

Many models have been proposed in the literature of traffic engineering to investigate the relations between the prominent traffic variables such as velocity, density, and flow. However, in a specific period and environment, the traffic variables do not follow the same pattern discovered by a model and the observations might have various behavioral patterns. This means that a wide range of tangled data must be investigated and the aforementioned diverse patterns should be extracted so that the future values of the traffic variables can be predicted. This paper proposes a new approach based on which the relations between the traffic variables in the tangled data are detected. To show the practical value of this study, different procedures of the proposed approach are carried out for the traffic data of a highway in Iran. To detect the existing patterns of the collected data, an optimization regression-based model is developed based on which the traffic observations are segregated. The objective of this model is minimization of the Mean Squared Error ( M S E) metric. The proposed approach fits multiple lines to the tangled data instead of a single one; therefore, the observations are segregated and diverse relations between the traffic variables will be revealed. The developed model is a mixed-integer non-linear mathematical formulation belonging to the class of NP-hard problems. Thus, two powerful meta-heuristics including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to solve the model and find the coefficients of the fitting lines. For a small number of observations, the outputs of the GA and PSO have been tested in terms of validity by comparing them to the optimal M S E values provided by the LINGO software. The GA and PSO could reach the optimal values in 77% and 64% of their runs, respectively. The validity of the results have also been confirmed by conducting one sample t-tests. The coefficient of determination R 2 has been utilized to evaluate the goodness of the fitting lines acquired by the GA and PSO. The outputs show that the R 2 values of all fitting lines are above 0.80; therefore, it can be concluded that the GA and PSO have been successful in delivering high-quality fitting lines. Having acquired high-quality fitting lines, it was proven that the proposed approach has been sufficiently efficient and effective in detecting various behavioral patterns existing in the traffic tangled data. [ABSTRACT FROM AUTHOR]

The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software's capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model's characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models' characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library. [ABSTRACT FROM AUTHOR]

The implementation of conventional supply-based mechanisms, in which the allocation of irrigation water entitlement for individual farmers is determined by their specific water requirements, has resulted in the inefficiency of economic processes and the squandering of water resources. This paper introduces a new dynamic bi-objective equity framework within an irrigation system. The framework aims to minimize the disparity of water supply per net benefit at the upper level and optimize the disparity of water shortage at the lower level. In order to obtain a feasible and satisfactory outcome, the study considers the use of fuzzy goal programming and Improved Particle Swarm Optimization (IBPSO) to analyze the initial data gathered from Sistan Basin, a real arid area in Iran. The uncertainty surrounding inflow, planting area, and irrigation coefficient has presented potential challenges for the irrigation process. As a result, new adaptation measures are being explored to mitigate the effects of these challenges. The following study examines the equity of the irrigation system under various scenarios in light of the anticipated deterioration of climatic patterns in the coming years, and offers constructive insights. The final results indicate that watermelon has the highest water consumption, whereas grapes exhibit lower sensitivity to water availability. The adoption of adaptation strategies, such as transitioning from water-intensive and low-value watermelon cultivation to alternative crops like wheat, could significantly enhance water conservation efforts in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

As crucial as it is to monitor the water quality in the reservoirs, constant measurement of different water quality indices can become a serious challenge. One way to alleviate this predicament is to create accurate numerical models and yet, the modeling process itself can have various problems that need to be addressed effectively. One major problem regarding water quality modeling is the high number of parameters and the uncertainty accompanying them. Eutrophication, being a significant water quality issue in the water reservoirs, is no exception and is related to a multitude of parameters. This paper presents an uncertainty-based auto-calibrated two-dimensional water quality and hydrodynamic (CE-QUAL-W2) model which was developed to simulate the eutrophication phenomenon in the Karkheh Reservoir in Iran. Automatic calibration of the CE-QUAL-W2 model using the Sequential Uncertainty Conformity Algorithm (SUFI-2) resulted in 11, 12.8, 10, 14.7, and 37.5 percent lower root mean square error (RMSE) for dissolved oxygen, total phosphorus, NH4, NO3-NO2, and Chlorophyll-a, respectively, compared to the results obtained by the particle swarm optimization (PSO). Furthermore, 76 percent of the measured data were in a 95 percent confidence interval as a result of calibration with the SUFI-2. In addition, the SUFI-2 algorithm outperformed the PSO algorithm within just 250 function evaluations, which was 20 times fewer than what was required by the PSO. Based on the results, it seems that the SUFI-2 algorithm can help the calibration process to achieve a more accurate and reliable model while decreasing the computational burden of the modeling process. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to formulate the problem of optimal design of renewable wind/solar/biomass hybrid system for grid-independent applications in a region of Iran and to compare the genetic algorithm (GA) and performance of particle swarm optimization (PSO) on this optimization problem. There are many types of research on solar and wind hybrid energy systems, but research on solar/wind/biomass hybrid energy systems is rare. The biomass energy system can be used as a support and complementary system along with wind and solar energy systems. This paper studies the optimum design of a biomass/PV/wind energy system for independent applications. The objective of the optimum design problem is to minimize the total net present cost (TNPC) of the PV/wind/biomass system during its lifetime subject to some constraints by adjusting three decision variables, namely the swept area of wind turbines, the area of PV panels and the capacity of biogas generators. For this aim, two efficient metaheuristic techniques of GA and PSO are used to solve the optimization problem. Simulation results show that PV/biomass system is the most cost-effective one for supplying the demanded load. Moreover, PSO leads to better results than GA. [ABSTRACT FROM AUTHOR]

The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

This paper proposes a multiobjective optimization approach for a preventive maintenance scheduling problem. To model this problem, an optimization model was developed by considering two objective functions: minimizing maintenance cost associated with the preventive tamping cost, machine preparation cost, and possession cost during time scheduling, as well as minimizing the total loss of remaining useful life (LRUL) over a given planning horizon. The applicability of the model was tested with the strength Pareto evolutionary algorithm II (SPEA-II) and multiobjective particle swarm optimization (MOPSO) algorithms in a case study on a section of Tehran-Mashhad line in Iran for the planning horizon of three years. Finally, a sensitivity analysis was performed on the possession cost and maximum permitted train speed. The results showed that by increasing the maintenance budget up to 47.32%, the track capacity could be increased by 25% due to increased permitted speed. Moreover, by increasing the cost associated with track possession, the preparation cost was reduced by considering the opportunistic maintenance policy through the grouping track segments. [ABSTRACT FROM AUTHOR]

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM). [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first case was identified in Wuhan, China, in December 2019. The number of confirmed cases is increasing daily and reached 101 million on 28 January 2021. This paper attempts to propose a new hybrid intelligent method for the prediction of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded. To forecast future cases, wavelet full decomposition of time series analysis served as input data for Adaptive Network-based Fuzzy Inference System (ANFIS). In addition, to tune the ANFIS membership functions, Quantum-behaved Particle Swarm Optimization (QPSO) was used. During the data preprocessing phase, all kinds of Wavelet Transform functions were tested for the best result. The proposed method was found to be very efficient in forecasting confirmed cases of COVID-19 in the upcoming ten days. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using QPSO and Wavelet Decomposition. The WT-QPSO-ANFIS model is evaluated using the World Health Organization (WHO) official data on the outbreak of COVID-19 to forecast the confirmed cases in the upcoming ten days. More so, the WT-QPSO-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R2), and computing time. [ABSTRACT FROM AUTHOR]

The development of agricultural, industrial, and urban activities creates an increase in pollution loading in rivers that may violate water quality standards which in its turn results in damages to the river systems. An efficient model of waste-load allocation plays a significant role in improving the water quality of the rivers. In this paper, a cost-based waste-load allocation model (C-WLA) is applied to guarantee the optimal management of both costs and river water quality. To determine the optimal loading pattern and threshold limits, the trade-off between treatment cost and pollution loss from the river is considered. In this regard, the MIKE11 model is coupled with the Particle Swarm Optimization (PSO) algorithm and applied to the Karoon River in Iran to demonstrate its practicality and efficiency. The sum of water treatment cost and pollution loading loss is minimized and the monthly optimal treatment percentages and threshold limit were determined. Then, different strategies under various operations of the river system are given with insights into the impacts of the trade-off policy between costs and losses for the discharger's TDS removal. The results demonstrated that the C-WLA model can achieve optimal management of pollution load in various operating of the river system. In addition, it is also shown that the proposed method is expected to offer better decision support to reasonable waste-load allocation where it can encourage dischargers to improve loading performance. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved. [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]

The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions. [ABSTRACT FROM AUTHOR]

The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high importance in the management of surface water resources. In this paper, contemporary-developed regression analysis was proposed to estimate the hard-to-measure parameters from those that can be measured easily. To this end, we proposed a novel modification of support vector regression (SVR), known as multiple-kernel support vector regression (MKSVR) algorithm. The MKSVR learns an optimal data representation for regression analysis by either linear or nonlinear combination of some precomputed kernels. For solving the optimization problem of the MKSVR, the particle swarm optimization (PSO) algorithm was used. The proposed algorithm was assessed using WQPs taken from Karun River, Iran. MKSVR was used to estimate chemical oxygen demand (COD) and biochemical oxygen demand (BOD) using nine WQPs as the input variables, namely electrical conductivity, sodium, calcium, magnesium, phosphate, nitrite, nitrate nitrogen, turbidity, and pH. The results of the proposed MKSVR were compared with those obtained using the SVR and Random Forest regression (RFR). The results showed that the MKSVR algorithm (correlation coefficient [R] = 0.8 and root mean squared error [RMSE] = 4.76 mg/l) increased the accuracy level of BOD prediction when compared with SVR (R = 0.68 and RMSE = 5.15 mg/l) and RFR (R = 0.77 and RMSE = 5.15 mg/l). In the case of COD estimation, the performance of a developed support vector machine (SVM) technique was satisfying. Overall, the use of MKSVR along with the PSO algorithm could demonstrate the superiority of the newly developed SVM technique for the WQPs estimation in the natural streams. [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]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

The integrated production-distribution (P-D) planning has turned into one of the most essential areas in supply chain (SC) management in recent years, especially in the case of perishable products in which the quality of products can change over time. Nonetheless, so far, the suggested models have focused on the P-D stages of the chain while the delivery of high-quality products to customers is of paramount significance in the perishable SC. In the present paper, a multiobjective, mixed-integer, and nonlinear programming (MOMINLP) mathematical model was developed for integrated P-D deteriorating items in a two-echelon SC that emphasizes quality degradation. Quality is monitored and calculated as a function of temperature and time throughout the SC, and the main purpose of the model is to first increase the quality of products delivered to customers and, second, minimize the SC costs. To optimize the problem, the particle swarm optimization (PSO) approach was also incorporated into the model. The obtained model was applied to a case study in Protein Gostar Sina Company in Iran, which resulted in decreased P-D costs as well as increased customer satisfaction. [ABSTRACT FROM AUTHOR]

In this paper, a new version of the multi-objective particle swarm optimizer named the Diversity-enhanced fuzzy multi-objective particle swarm optimization (f-MOPSO/Div) algorithm is proposed. This algorithm is an improved version of our recently proposed f-MOPSO. In the proposed algorithm, a new characteristic of the particles in the objective space, which we named the "extremity," is also evaluated, along with the Pareto dominance, to appoint proper guides for the particles in the search space. Three improvements are applied to the f-MOPSO to mitigate its shortcomings, generating f-MOPSO/Div: (1) selecting the global best solution based on the diversity of the extreme solutions, (2) impeding the particles to be trapped in the local optima using a mutation scheme based on the dynamic probability, and (3) removing the pre-optimization process. To validate f-MOPSO/Div, it was compared with some other popular multi-objective algorithms on 14 standard low- and high-dimensional test problem suites. After the comparative results indicated the outperformance of the proposal, the f-MOPSO/Div was applied to solve an optimal conjunctive water use management problem, in a semi-arid study area in west-central Iran, over a 13-year long-term planning period with two main objectives: (1) maximizing the aquifer sustainability as an environmental goal, and (2) maximizing the crop yields as a socio-economic goal. As the results suggest, the cumulative groundwater level drawdown is considerably decreased over the whole planning period to make the aquifer sustainable, while the water productivity is held at a desirable level, demonstrating the superiority of the f-MOPSO/Div when also applied to solve a large-scale real-world optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, the efficient amounts of radiation received by a flat‐plate collector and top heat loss coefficient along with affecting parameters are investigated using measured data in different climates of Iran. Coded program in MATLAB software is then distributed to calculate the optimum tilt angle for extracting the highest solar radiation in different months of the year for five cities of Iran with different geographic latitudes. An optimal annual slope is obtained for maximum utilization and for conditions where the collector can only be adjusted at an angle during the year. Using an interpolation, an equation has been derived for obtaining the optimal tilt angle of collector for areas with latitude between 27° and 37°. Inasmuch as the top heat loss coefficient is required for evaluating the thermal performance of solar collectors, the value for different temperatures is obtained at ambient temperature up to 200°C for the absorber plate. Effects of key parameters such as collector slope, number of covers, wind speed, emittance coefficient of adsorption plate, and cover spacing on top heat loss coefficient are investigated. In the end, the particle swarm optimization (PSO) method is employed to minimize the top heat loss coefficient. The minimum amount of top heat loss coefficient is obtained with the amount of 0.99 W/m2K. [ABSTRACT FROM AUTHOR]

In this paper, for the first time an Improved Particle Swarm Optimization (IPSO) algorithm, is developed to evaluate the 2.5-D basement of sedimentary basin and consequently to simulate its bottom, by using the density contrast that varies parabolically with depth simultaneously. The IPSO method is capable of improving the global search of particles in all of the search fields. Finding the optimum solution is adjusted by an inertia weight and acceleration coefficients. Here, we have examined the ability of the IPSO inversion by the synthetic gravity data due to a sedimentary basin, with and without noise. The calculated depth and gravity of the synthetic model do not differ too much from assumed values due to set limits for model parameters and are always within the range. Also, the mentioned method has been applied for the 2.5-D gravity inverse modelling of a sedimentary basin in Iran. We also have modelled the sedimentary basin in 2-D along seven profiles. Furthermore, using the depth values estimated by IPSO from all profiles, a 3-D model of the sedimentary basin was generated. The obtained maximum depth for this sedimentary basin is 2.62 km. [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]

In the conventional zone-based hedging rule, as a common approach, the rationing factors are changed from one zone to another at once, which is not proper for actual reservoir operation. Hence a fuzzy-rule-based approach is employed to create a transition zone to assign rationing factors. This remedy causes gradual variation in the rationing factors and mitigates severe water shortages during drought periods. In this paper, a monthly simulation model linked to an evolutionary algorithm is developed. The proposed model was applied to the Zohre multireservoir system in southern Iran. There are two objective functions to supply minimum flow and agriculture demands over a long-term simulation period. Therefore, a multiobjective particle swarm optimization (MOPSO) algorithm was applied. The results showed that annual and long-term modified shortage index (MSI) values have been improved compared to the current conventional hedging rule. Implementation of the proposed hedging rule for the case study results in improving at least 34 and 21% in the maximum MSI, respectively, for the annual minimum flow and agriculture shortages. [ABSTRACT FROM AUTHOR]

Depth estimation of gravity anomalies is one of the most important geophysical problems in the exploration of mineral deposits. In the present paper, we try to estimate mass anomaly depth by using an artificial intelligence method called the Centred-Progressive Particle Swarm Optimisation (CP-PSO) with sample shapes such as a sphere, horizontal cylinder and vertical cylinder, which simulate the shape of most causative bodies. Using an artificial intelligence method is common for the case in ore bodies detection and delineation. When modelling gravity data, we estimate the depth and shape factor; therefore, we suppose depth (z) and shape factor (q) as particles in this algorithm. This technique was tested for synthetic models contaminated with random noise and the results are quite acceptable and promising. The proposed method was also successfully applied to real mineral exploration data. The desired location is near one of the outcroppings of the Safo manganese mine located in north-western Iran. The results show that the estimated depth and the shape factor model were in good agreement with the results obtained through the Euler method and drilling. [ABSTRACT FROM AUTHOR]

Nowadays, sea wave energy is widely regarded as an energy source that is clean, renewable and highly available for power extraction. The subject of extracting the maximum power from sea waves in Iran is of great importance due to access to the Caspian Sea and the Persian Gulf. Moreover, there is a need for resources with no air pollution besides providing a part of the country's power demand without costly infrastructure, so this research field is highly interesting although has rarely been addressed so far. The main purpose of this paper is to use an appropriate control strategy to improve the performance of point absorbers. In this scheme, to consider high uncertainty in the parameters of the power take-off system in different atmospheric conditions and improve controller performance, a new improved black hole algorithm is introduced to tune fuzzy controller parameters. The proposed method is then implemented for tuning the fuzzy controller parameters in order to obtain the maximum power capture of wave energy converters. Compared to particle swarm optimization and conventional black hole algorithm, the results of the proposed method indicate enhancements in reference velocity tracking and absorbed power. Finally, some simulations are performed and the proposed controller is implemented for the wave spectrum of the Persian Gulf waters, so the performance of the proposed controller is evaluated. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry. Design/methodology/approach: To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities. Findings: Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy. Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable. Originality/value: This study was one of the first in-house studies to compare the results of metaheuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers. [ABSTRACT FROM AUTHOR]

The optimization of hydropower multi-reservoir systems is one of the most challenging reservoir management problems due to the complex dynamic nonlinear influencing variables such as reservoir active storage volume, released water discharge rate, and water pressure head available for hydropower generation. To optimize such a complex problem in an efficient way, the development of a high-precision meta-heuristic algorithm are of essential significance. This paper develops an adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm to derive optimal operating rules for multi-reservoir systems with the purpose of hydropower production. A-DEPSO algorithm uses novel mutation and crossover mechanisms to improve global and local search capabilities, respectively. The application of the proposed method is verified to solve a complex four-reservoir hydropower generation system, located in the southwest of Iran. The results achieved indicate that the A-DEPSO reduced the value of the objective function by an average of 57% compared with other well-known optimizers in the literature. Furthermore, the total power generated from the four-reservoir system using the proposed method increased by an average of 11% compared to the other methods. Finally, the results obtained in this study confirm that the A-DEPSO can be used efficiently to solve complex multi-reservoir hydropower generation systems. • An adaptive DE with PSO (A-DEPSO) is proposed. • A novel mutation and crossover operator are introduced. • The performance of the A-DEPSO is evaluated by a hydropower 10-reservoir problem. • A-DEPSO is used to optimize the operating rules for a hydropower four-reservoir system. [ABSTRACT FROM AUTHOR]

In this paper, the design of a hybrid renewable energy PV/wind/battery system is proposed for improving the load supply reliability over a study horizon considering the Net Present Cost (NPC) as the objective function to minimize. The NPC includes the costs related to the investment, replacement, operation, and maintenance of the hybrid system. The considered reliability index is the deficit power-hourly interruption probability of the load demand. The decision variables are the number of PV panels, wind turbines and batteries, capacity of transferred power by inverter, angle of PV panels, and wind tower height. To solve the optimization problem, a new algorithm named improved crow search algorithm (ICSA) is proposed. The design of the system is done for Zanjan city, Iran based on real data of solar radiation and wind speed of this area. The performance of the proposed ICSA is compared with crow search algorithm (CSA) and particle swarm optimization methods in different combinations of system. This comparison shows that the proposed ICSA algorithm has better performance than other methods. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid of lightning search algorithm (LSA) and non-dominated sorting genetic algorithm II (NSGAII) is proposed in order to increase the accuracy and efficiency of the conventional multi-objective NSGAII. In the proposed method, the important parameters of the NSGAII are optimized using LSA. To verify the effectiveness of the proposed algorithm, it is applied to 19 well-known standard test functions, including unconstrained functions, UF1–UF7, constrained functions, CF1–CF7, Schaffer1, Schaffer2, ZDT1, ZDT2, and ZDT3. The inverted generational distance (IGD) is used to check the efficiency of the proposed algorithm. Accordingly, the proposed algorithm is compared with the most used multi-objective algorithms. Besides, the proposed algorithm is applied to a practical case study of optimal switch, including reclosers and disconnectors, placement in the presence of distributed generation (DG) sources. The distribution networks are the Roy-Billinton test system (RBTS) and a part of the real network in Mazandaran province, in Iran. The simulation results confirm the superiority of the proposed method. For example, in practical case studies, the proposed algorithm for optimal recloser placement in the real network shows 47.09% and 4.691% cost improvement compared with multi-objective particle swarm optimization (MOPSO) and NSGAII, respectively. These improvements for RBTS-BUS2 are 3.725% and 3.502%, respectively. Also, for the RBTS-BUS2, the proposed algorithm result shows 1% and 0.732% reliability improvement compared with MOPSO and NSGAII, respectively. • A new hybrid meta-heuristic multi-objective algorithm is proposed. • Lightning search algorithm, optimizes the important parameters of NSGAII. • The proposed algorithm is validated by using of 19 test problems. • The proposed method is applied on two practical optimization problem. • Optimal recloser placement is done by using the proposed HLSA-NSGAII. [ABSTRACT FROM AUTHOR]

• A bi-level mathematical model is firstly formulated for the rice supply chain. • Two-hybrid and a modified metaheuristic algorithms are developed. • A real-life applied example is provided. • Results of numerical experiments demonstrate the efficiency of the modified algorithm. According to the recent gigantic development in the agricultural section, Agricultural Supply Chain (ASC) management has attracted both researchers and agronomic practitioners. In this regard, rice as one of the important agricultural products is generally cultivated by rural farmers in small farmlands. Due to the high demand, high price, type of products, and also wide geographic range of production and consumption, the rice supply chain has special characteristics in ASC. In this regard, this paper not only firstly considers rice supply chain and but also proposes a bi-level optimization model for rice supply chain. The proposed model aims to minimize total cost with respect to the two decision makers' opinions. Since, the bi-level programming is NP- hard , to solve the proposed model, two well-known meta-heuristic algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) along with two hybrid algorithms (PSO-GA and GA-PSO) and a modified algorithm (GPA) are utilized. In order to fill the literature gaps and to get closer to real-world applications, an applicable example in Iran is studied. Based on the results and managerial insights, the GPA is chosen as the best method and its allocated value of the variable are reported. The results show that the proposed model and solution methods are valid, practical, and effective. Also, in order to provide an insight to the functionality of the model and the results of the case, some sensitivity analyses on the major model parameters such as the demand are provided. [ABSTRACT FROM AUTHOR]

Highlights • Monthly precipitation was predicted using soft computing methods including ANFIS with GA, ACOR, PSO and DE. • Suggested algorithms improved the ANFIS performance for modeling the precipitation. • Suggested methods decreased the event of trapping in local optima. • ANFIS-ACOR model with least error and most correlation showed highest performance in monthly precipitation forecast. Abstract Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACO R), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACO R , ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACO R (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters. [ABSTRACT FROM AUTHOR]

In this paper, the latest approaches for automated history matching (AHM) were applied to a real brown field having 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in the south of Iran. A modified support vector machine was employed to create a proxy model incorporated based on design of experimental. Thereafter, all model parameters were adjusted to reproduce the observed history within the created proxy model. Accordingly, the proposed proxy model was successfully constructed using 1086 samples based on an R2 coefficient of about 0.9 for the trained and test dataset. Finally, the process was optimized by two main algorithms to reach the best solutions, which are genetic and particle swarm optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • New designing framework for hybrid PV/WT/Battery system considering uncertainty. • Application of Cloud theory to model the wind, solar, and load uncertainty. • Considering the effect of battery degradation cost in the hybrid system design. • Using of a new meta-heuristic algorithm named OBLGBO for sizing the hybrid system. • Evaluating the superiority of the OBLGBO compared with GBO, GWO,and PSO. This paper performs a new optimal framework for a hybrid photovoltaic-wind system design integrated with battery storage (PV/WT/Battery), considering cloud-based uncertainty modeling and battery degradation based on real meteorological data from the Sarein-Ardabil region in Iran. The objective function is presented as minimizing the total net present cost (NPC), load loss, and battery degradation cost. The decision variables include the number of PVs, WTs, batteries, inverter power, and the angle of PVs installation, which is optimally determined via a new meta-heuristic optimization algorithm named opposition-based learning and Gradient-based optimizer (OBLGBO). In the proposed framework, the cloud theory method based on combining fuzzy theory and probability statistics has been applied for modeling the energy resources and load demand uncertainties. The simulation results indicate that considering battery degradation costs increases the overall cost of designing hybrid systems. As the cost of degradation increases, reliability indices improve due to an increase in the number of wind turbines and a decrease in the number of batteries. Also, the results demonstrated that incorporating the uncertainties based on the cloud theory increases the design cost, and the system reliability is weakened. Therefore, the proposed optimal framework has presented a real and accurate approach to the optimal design of energy systems with more accurate knowledge of electricity generation costs and the cost of improving reliability in conditions of uncertainties. Moreover, the superior capability of the OBLGBO compared with traditional GBO and the well-known particle swarm optimization (PSO) and grey wolf optimizer (GWO) is proved to achieve lower design cost and better reliability indices. [ABSTRACT FROM AUTHOR]

Access to sufficient and safe water particularly in vulnerable countries is one of the most important goals targeted by the United Nations for 2030. The agricultural water supply is crucial in arid areas such as Iran. In water supply projects, employers usually seek less duration and cost, and higher quality, however, contractors seek a reduction of fluctuations in project resources, especially human resources, and these goals may be in conflict with each other. Therefore, finding an optimal project schedule considering different project objectives is vital for managers to achieve stakeholders' satisfaction. In this study, the four main project objectives including time, cost, quality, and resource leveling as well as the activity dependencies and resource constraints are addressed through fuzzy optimization. The proposed model was first implemented on a small-sized project instance for validation; then, the model was solved by the Nondominated Sorting Genetic (NSGA-II) and Multi-objective Particle Swarm Optimization algorithms using the information of a real-world agricultural water supply project. The results of the first Pareto set for different alpha-cuts in relation to the fuzzy quality factor show a set of solutions and different alternatives for project implementation which leads to a number of execution modes each of which may result in achieving the project objectives. Finally, the most ideal solution with a duration of 178 days was selected using the fuzzy TOPSIS method. The findings demonstrate the validity of the proposed model concerning the needs of agricultural water supply organizations. [ABSTRACT FROM AUTHOR]

The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young's Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson's ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young's modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson's ratio. The root mean square error (RMSE) and determination coefficient (R2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs. [ABSTRACT FROM AUTHOR]

Scientific prediction of TBM tunneling efficiency has important guiding value and significance for long-distance tunnel construction risk and project cost control. A TBM tunneling speed prediction model based on the least squares support vector machine algorithm optimized by the improved particle swarm optimization algorithm is proposed. The standard particle swarm optimization algorithm is improved by using adaptive inertia weight, and the regularization parameters and kernel parameters of LSSVM are optimized based on the improved PSO algorithm. The tunneling speed prediction of TBM is trained by using the tunnel data of No.3 tunnel in Queens, New York and Karagyi-Tehran diversion tunnel in Iran. After the training reaches a certain accuracy, the actual engineering data of TBM construction of long tunnel in Xinjiang are predicted. The results show that the proposed IPSO-LSSVM model has a prediction fitting degree of 0.9776 for TBM tunneling speed and an average absolute error of 1.0771%, which is more accurate than the prediction results of SVR, LSSVM and PSO-LSSVM models. The prediction error does not exceed 1.5% in practical engineering. [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]

The aim of this study was investigating the effect of drought on groundwater storage and measuring the impact of future drought events under climate change conditions on Najafabad aquifer in Iran. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. Various meteorological and hydrological drought indices were considered as model inputs and the best combination of drought indices was selected by the Gamma Test (GT) method. The drought analysis revealed that the Groundwater Resource Index (GRI) and the Reconnaissance Drought Index (RDI) were the most effective indices in modelling the aquifer. To examine future drought events under climate change, the outputs of five ISI-MIP5 (Inter-Sectoral Impact Model Inter-comparison Project) General Circulation Models (GCMs) were derived under two Representative Concentration Pathways (RCPs). The results indicated that the most severe droughts will occur during the years 2035–2039 and 2033–2041 under RCP4.5 and RCP8.5, respectively. Continuing the current aquifer withdrawal policy, the groundwater storage will be affected by future drought events and decrease by 8.8% and 9.5% compared to the base period (1990–2016) under RCP4.5 and RCP8.5, respectively. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is still an ongoing requirement for variety of applications such as land use management plans. The central objective of the present research was to investigate the effect of using ensemble machine learning methods for developing accurate landslide prediction. We aimed to explore and compare three techniques, namely the random forests, support vector machine and multiple-layer neural networks with an adaptive neuro-fuzzy inference system, which incorporates three metaheuristic methods including grey wolf optimization, particle swarm optimization, and shuffled frog leaping algorithm for landslide susceptibility assessment in the East Azerbaijan of Iran. Also, two ensemble ways (voting and stacking) were used in final decision stage. A sum of 766 locations with landslide inventory was recognized in the context of the study. Then the all models were trained using tenfold cross-validation technique. Lastly, the receiver operating characteristic and statistical procedures were employed to validate and contrast the predictive capability of the models. The findings of the study show the ANFIS–PSO model had high performance on the validation dataset (AUC = 0.89). Besides, the study revealed that using stacking ensemble technique could increase the predicting capability of all models (AUC = 0.911). [ABSTRACT FROM AUTHOR]

In this paper, a new methodology is proposed for waste load allocation in river systems using the decision support system (DSS) for the graph model for conflict resolution II (GMCRII), multi-criteria decision making (MCDM) analysis and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Minimization of total treatment and penalty costs and minimization of biological oxygen demand violation of standards at the check point are considered as the main objectives of this study. At first, the water quality along the river was simulated using the Streeter-Phelps (S-P) equation coupled with the MOPSO model. Thereby a trade-off curve between the objectives is obtained and a set of non-dominated solutions is selected. In the next step, the best alternative is chosen using MCDM techniques and the GMCRII DSS package and non-cooperative stability definitions. The applicability and efficiency of the methodology are examined in a real-world case study of the Sefidrud River in the northern part of Iran. [ABSTRACT FROM AUTHOR]

The aim of the present research is to estimate the saffron yield by land characteristics through a combination of adaptive neural-fuzzy inference system and particle swarm optimization in Siminehrood catchment, south of Urmia Lake, Iran. To achieve this target, 150 representative soil profiles were descripted in saffron fields. Then each genetic horizon was sampled for soil analysis. Climate rating was calculated from meteorological data by Food and Agriculture Organization framework. Saffron observed yield obtained from saffron field data. The results showed that coarse fragment, gypsum, electrical conductivity, organic carbon, cation exchange capacity, pH, calcium carbonate equivalent and climate rating have the highest correlation with saffron yield. The range of saffron estimated yield was between 1937–4124 and 1843–4025 kg per hectare for combination model and fuzzy model, respectively. Combination model doses a more accurate estimation compared with the observed yield values (2020–4200), statistical validation indicator results confirm this too. High agreement obtained from combination model between estimated saffron yield map with observed yield map. Finally, a combination of adaptive neural-fuzzy inference system and particle swarm optimization model can be employed as a powerful, low time-consuming and accurate method for estimating saffron yield in pre-decision for saffron cultivation. [ABSTRACT FROM AUTHOR]

Rate of penetration (ROP) is one of the most important parameters in reducing drilling expenditure. In this paper, a ROP management study has been conducted for a well in Southwest of Iran. As a part of this study, the best approach for ROP prediction was determined by comparing the performance of several methods and particle swarm optimization (PSO) algorithm was implemented for optimization of ROP. This paper highlights the effects of formation thickness and the magnitude of working data-set on the performance of different algorithms which are being used for ROP management studies, especially in horizontal and inclined wells. This topic has special importance, when the results show a massive difference in thin and thick formations for some ROP prediction methods. Comparing the results of different ROP prediction methods showed that hybrid Bingham model has the best performance in ROP prediction; only if a powerful mathematical tool like trust-region method is being used for the determination of its unknown coefficients. This superiority was not generalized in all individual formations and there were different results in the cases of thin and thick formations; so, application of the bag-of-algorithms strategy by using the most accurate method in each formation is suggested for the prediction of ROP. Performing this ROP management project resulted in the prediction of ROP with a total relative error of 13.59% and also 48.30% improvement in ROP. • The magnitude of working data-set affects the performance of ROP management algorithms. • This study expands the coverage of ROP prediction methods to horizontal and inclined wells. • Hybrid Bingham model in combination with trust-region method shows the best performance in ROP prediction. • Application of the bag-of-algorithms strategy is suggested for the prediction of ROP. [ABSTRACT FROM AUTHOR]

Drought, as a phenomenon that causes significant damage to agriculture and water resources, has increased across the globe due to climate change. Hence, scientists are attracted to developing drought prediction models for mitigation strategies. Different drought indices (DIs) have been proposed for drought monitoring during the past few decades, most of which are probabilistic, highly stochastic, and non-linear. The present study inspected the capability of various machine learning (ML) models, including artificial neural network (ANN) and support vector regression (SVR) as original predictive models and optimized by two selected algorithms, namely, particle swarm optimization (SVR-PSO) and response surface method (SVR-RSM) to predict the meteorological drought indices of standardized precipitation index (SPI), percentage of normal precipitation (PN), effective drought index (EDI), and modified China-Z index (MCZI) on a monthly time scale. A novel model named SVR-RMS is introduced by using two calibrating processes given from RSM with two inputs and the SVR by predicted data handled with RSM given from the first calibrating procedure. For evaluating the models, different meteorological input variables in the period 1981–2020 were considered from 11 synoptic stations in arid and semi-arid climates of Iran, which frequently experience droughts. The SPI showed the highest and lowest correlation with MCZI (0.71) and EDI (0.34), respectively. The results of testing dataset (2011–2020) indicated that the SVR-RSM produced superior abilities for both accuracy and tendency compared to other models, while the SVR-PSO model is better than the ANN and SVR. The worst results of drought prediction were obtained for EDI. However, all models provided the acceptable EDI prediction in the high-temperature station of Ahvaz in the south of the country. Application of SVR-RSM as a novel hybrid model can be suggested for predicting the DIs on a short time scale in arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g. remote sensing), the application and integration of spectroscopic data with those environmental variables remain limited. Hence, this study combines visible and nearinfrared (Vis-NIR) spectroscopy, remote sensing, and topographic data and applies random forests, hybridized with particle swarm optimization algorithm (RF + PSO), to predict the spatial variability of soil clay content, electrical conductivity (EC), and calcium carbonate equivalent (CCE) for 370km² of agricultural land in western Iran. Using a conditioned Latin hypercube approach, 220 soil samples at the 0–20 cm depth increment were acquired throughout the study area. Three sets of environmental covariates were tested: Scenario A (Vis-NIR spectroscopy data), Scenario B (environmental data), and Scenario C (Vis-NIR spectroscopy þ environmental data). According to the 10-fold crossvalidation procedure with 100 replications, the RF + PSO model showed an acceptable level accuracy for all scenarios, although the accuracy of the RF + PSO model using the Scenario C data was higher than all other scenarios: the Lin’s Concordance Correlation Coefficient values were 0.77, 0.83, and 0.74 for the clay contents, EC, and CCE, respectively. The results demonstrated that the combination of Vis-NIR spectroscopic data and commonly available environmental covariates provided the best input data for the hybridized model and enhanced its performance. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient methodology for the simulation-based multi-objective optimization problems, which addresses important limitations for the optimization of the building energy performance. In this work, a mono- and multi-objective particle swarm optimization (MOPSO) algorithm is coupled with EnergyPlus building energy simulation software to find a set of non-dominated solutions to enhance the building energy performance. To evaluate the capability and effectiveness of the approach, the developed method is applied to a single room model, and the effect of building architectural parameters including, the building orientation, the shading overhang specifications, the window size, and the glazing and the wall material properties on the building energy consumption are studied in four major climatic regions of Iran. In the optimization section, mono-criterion and multi-criteria optimization analyses of the annual cooling, heating, and lighting electricity consumption are examined to understand interactions between the objective functions and to minimize the annual total building energy demand. The achieved optimum solutions from the multi-objective optimization process are also reported as Pareto optimal fronts. Finally, the result of multi-criteria minimization is compared with the mono-criterion ones. The results of the triple-objective optimization problem point out that for our typical model, the annual cooling electricity decreases about 19.8–33.3%; while the annual heating and lighting ones increase 1.7–4.8% and 0.5–2.6%, respectively, in comparison to the baseline model for four diverse climatic regions of Iran. In addition, the optimum design leads to 1.6–11.3% diminution of the total annual building electricity demand. The proposed optimization method shows a powerful and useful tool that can save time while searching for the optimal solutions with conflicting objective functions; therefore facilitate decision making in early phases of a building design in order to enhance its energy efficiency. [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]

Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB. [ABSTRACT FROM AUTHOR]