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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

[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]

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]

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]

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]

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]

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]

The study of production systems taxonomy not only provides a good description of organization dominant groups but also provides the ground for more specialized studies such as a study of performance, the proper form of production decisions in each group, and the theorizing in it. In some taxonomic studies, due to high speed and ease of implementation, K-means cluster analysis was used to analyze data but the convergence took place in local optimum. For this reason, Hybrid Clustering Algorithms were used for the clustering of manufacturing companies. The clustering results of these methods were compared using validation indicators. According to the results of the comparisons, the best clustering algorithm was chosen, based on which cluster naming was done. Then, using the results of Discriminant Analysis, the distinctive dimensions of clusters were identified and the results showed that the manufacturing systems in Iran can be introduced in two dimensions of green production planning and resource capacity. [ABSTRACT FROM AUTHOR]

Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights; (2) modified rates by Wilcoxon tests and original weights; and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO3, SO4, and toxic metals, were considered. The results indicated that the DRASTICA model yielded more accurate contamination concentrations for vulnerability evaluations than the SI model. Moreover, both models initially displayed well-matched results for the SO4 concentrations, specifically 0.7 for DRASTICA and 0.58 for SI, respectively. Comparatively, the DRASTICA model showed a higher correlation with NO3 concentrations (0.8) than the SI model (0.6) through improved steps. Furthermore, although both original models demonstrated less correlation with toxic metal concentrations (0.05) compared to SO4 and NO3 concentrations, the DRASTICA and SI models with modified rates and optimized weights exhibited enhanced correlation with toxic metals of about 0.7 and 0.2, respectively. [ABSTRACT FROM AUTHOR]

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided. [ABSTRACT FROM AUTHOR]

Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio. [ABSTRACT FROM AUTHOR]

Shear-wave velocity (V s) is a key petrophysical data for a wide spectrum of applications in the upstream oil industry. In many wells, however, the corresponding log cannot be acquired due to technical and/or cost-related issues. Appreciating the importance of this parameter, its relationship to other petrophysical logs has been extensively studied. For the most part, such studies focus on modes based on either rock physics, analytic equations, or artificial intelligence (AI). Inherent complexity of hydrocarbon reservoirs, especially carbonate ones, has made it difficult to build a comprehensive model of adequately high accuracy for various fields, keeping the research on novel models for such a purpose a still hot topic. This paper presents a high-accuracy high-generalizability model for predicting V s from logging data. The required logs were acquired along three wells penetrating three carbonate reservoirs in SW Iran. In a preprocessing step, a robust regression technique was applied to identify and omit outliers. Subsequently, the data at two wells were used for training the model, with the data at the third well used for validating the trained model. Feature selection was performed by NSGA-II and five parameters were selected (V p , Depth, RHOB, NPHI, and RT) as inputs to the model. For the first time, we employed the convolutional neural network (CNN) and multilayer extreme learning machine (MELM) in simple and hybrid forms with a few optimization algorithms, including particle swarm optimization (PSO), cuckoo optimization algorithm (COA), and genetic algorithm (GA) to build different models for predicting V s from logging data. For the sake of comparison, we further applied the least-squares support-vector machine (LSSVM) in simple and hybrid forms with COA, PSO, and GA as well as a couple of popular analytic methods. Results of the training showed the superiority of the CNN, as measured by RMSE. Nevertheless, the MELM-COA model provided for much shorter learning time although its RMSE was only marginally higher than the CNN. Results of the testing phase showed better generalizability and accuracy of the MELM-COA. The same outcome was confirmed in the validation phase. A comparison between the developed IAs and well-known empirical equations showed the higher performance of the IAs. Accordingly, the methodology proposed in this study is highly recommended for estimating the shear-wave velocity at other wells across similar fields provided the model can be trained on a larger set of training data. • Data were gathered from three wells drilled in the carbonate reservoirs in southwest of Iran. • Several artificial intelligence (AI) algorithms and empirical correlations were used to predict Shear wave velocity. • Hybrid algorithms are superior to empirical correlations in shear wave velocity estimation. • Multi-layer extreme learning machine has the highest precision among used AI algorithms. [ABSTRACT FROM AUTHOR]

Forecasting the groundwater level is crucial to managing water resources supply sustainably. In this study, a simulation–optimization hybrid model was developed to forecast groundwater levels in aquifers. The model uses the PSO (Particle Swarm Optimization) algorithm to optimize SVR (Support Vector Regression) parameters to predict groundwater levels. The groundwater level of the Zanjan aquifer in Iran was forecasted and compared to the results of Bayesian and SVR models. In the first approach, the aquifers hydrograph was extracted using the Thiessen method, and then the time series of the hydrograph was used in training and testing the model. In the second approach, the time series data from each well was trained and tested separately. In other words, for 35 observation wells, 35 predictions were made. Aquifer's hydrograph was evaluated using the forecasted groundwater level in the wells. The results showed that the SVR-PSO hybrid model performed better than other models in terms of Root Mean Square Error (RMSE) and coefficient of determination ( R 2 ) in both approaches. In the first approach, the SVR-PSO hybrid model forecasted the groundwater level in the next month with a training RMSE of 0.118 m and testing RMSE of 0.221 m. In the second approach, using the SVR-PSO hybrid model, the RMSE error was reduced in 88.57% of the wells compared to other models, and more reliable results were achieved. Based on the performance, the SVR-PSO hybrid model can be used as a tool for decision support and management of similar aquifers. [ABSTRACT FROM AUTHOR]

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data. [ABSTRACT FROM AUTHOR]

Grade estimation is a critical issue in mineral resource evaluation, being extensively investigated by data mining techniques. In this paper, a hybrid method composed of back-propagation artificial neural network (BPANN) and particle swarm optimization (PSO) algorithms is proposed to solve the grade estimation problem. The PSO algorithm is implemented to optimize the BPANN parameters by reducing the effects of a local minimum problem, which is one of the critical drawbacks of BPANN. The proposed BPANN-PSO algorithm is validated for Al 2 O 3 grade estimation in one of Iran's largest Bauxite deposits. The performance of BPANN-PSO algorithm for grade estimation is compared with BPANN and ordinary kriging. The experimental results indicate that the BPANN-PSO model is more appropriate for estimating Al 2 O 3 grade with a reasonable error. • 3D block model is estimated by a hybrid model of BPANN and PSO algorithms. • Samples coordinate and local variability are used in the BPANN architecture. • The initial weight and threshold value of the back-propagation artificial neural network are optimized by PSO. • The proposed model showed better performance compared to the ordinary kriging and BPANN models in the presented case study. [ABSTRACT FROM AUTHOR]

The present research, for the first time, introduces a sustainability‐based water quality–quantity management model in the river–reservoir system. Vulnerability, resiliency, reliability and maximum deficit are the factors that have been considered in the Sustainability Index (S.I.). The two‐dimensional hydrodynamics and water quality simulation model (CE‐QUAL‐W2) is linked to the multi‐objective particle swarm optimization (MOPSO) to develop a simulation‐optimization approach. Also, artificial neural network (ANN) model, substituted for the Ce‐QUAL‐W2 model, reduces the computational time in adaptive dynamically refined routine. The proposed methodology is applied to the Karkheh river–reservoir in Iran. The results showed that using ANN in an adaptive form, replacing CE‐QUAL‐W2, significantly impacts the computational time, considering accuracy in the developed simulation‐optimization model. Moreover, using the S.I. greatly reduces both the severity of failure periods in the water quantity–quality and the sequence of failure periods. [ABSTRACT FROM AUTHOR]

With intense human activity causing constant environmental change, there is a greater need than ever to have accurate and frequently updated soil information. Traditional soil maps are large‐scale polygon maps of soil type and implicitly assume no variation in soil properties within polygons. DSM approaches can increase the accuracy of modelling. Therefore, the current study was conducted to evaluate the applicability of a classic adaptive neuro‐fuzzy inference system (ANFIS) and three hybridized ANFIS models (i.e. particle swarm optimization—PSO, the genetic model—GA and the artificial bee colony model—ABC) for predicting eight soil properties in western Iran. The ANFIS+ABC and ANFIS+PSO approaches showed the best performance in predicting soil properties such as soil organic carbon (SOC) and total nitrogen (N). The ABC+ANFIS‐hybrid model increased R2 values by 0.32, 0.24, 0.23 and 0.37 and decreased RMSEs by 11%, 32.7 mg kg−1, 0.08 mmhos cm−1 and 0.3% when predicting SOC, K, EC and SP, respectively, compared with the ANFIS model alone. Also, the use of the PSO+ANFIS‐hybrid model led to an increase in R2 values of 0.44, 0.29, 0.34 and 0.36 and a decrease in RMSEs of 0.02, 0.29 ppm, 0.34% and 0.36% for predicting N, P, pH and TNV, respectively, compared with the ANFIS model alone. In general, the ANFIS+ABC and ANFIS+PSO improved the prediction accuracies for SOC and N by 32% and 24%, respectively, compared with the classic ANFIS model. As a whole, our findings demonstrated the ability of hybrid models for improving the accuracy of the ANFIS approach for predicting soil features in western Iran, which supports our main hypothesis. [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]

Emergency evacuation throughout and after the flood is a crucial task to mitigating more instantaneous impacts, whilst refining social resilience for longer-term recovery. To enhance the evacuation process, the determination and prediction of safe areas before a flood is necessary. Indeed, the safe area or shelter in place could play two roles during the flood; as temporary shelters and as meeting points (station) for gathering before evacuation. This paper aims to determine the safe area according to the spatial and environmental characteristics of the urban extent (accessibility, topography, congestion, and land use). The main contribution is finding safe areas using modified particle swarm optimization (MPSO) with local search (LMPSO). The proposed method recognizes the optimal location of temporary shelters as evacuation stations. It has been implemented in Districts 3, 6, and 7 of Tehran, the capital of Iran. The comparison between the achieved results of MPSO and LMPSO demonstrated that the LMPSO is more efficient than the modified version. Since LMPSO is less sensitive to local minima and converged to minimum cost faster than MPSO, and the distribution of optimum locations of safe areas has been balanced, so all the population could benefit from these stations. The comparison among the results of MPSO, LMPSO, GA, ACO and genetic simulated annealing algorithms justified the efficiency of LMPSO too. • A new approach for finding safe areas as evacuation stations in flood is introduced. • Two strategies are planned using basic and local search particle swarm optimization. • The results are assessed due to the performance and distribution of detected places. • The evaluation confirmed that the LMPSO is more efficient than applied algorithms. [ABSTRACT FROM AUTHOR]

Hydrological uncertainties are the main components of a sustainable framework in agricultural water management. Prediction of drought as a meteorological phenomenon should be considered to define the groundwater exploitation strategies. This study was conducted to develop a multiobjective-bivariate structure for reducing the soil moisture deficit and groundwater withdrawal in the Qazvin Irrigation District, Qazvin province, Iran. Therefore, non-dominated sorting theory, self-organizing particle swarm optimization and bivariate copula functions were incorporated under fuzzy uncertainty analysis. The results showed that the generalized extreme values and log-normal distribution functions had the best fitness on the drought peak and severity with Kolmogorov Smirnov amounts of 0.08 and 0.17, respectively. Furthermore, the goodness-of-fit tests were indicated the Joe joint function (MLE = 11) is the appropriate function for estimating the probabilistic values of drought characteristics. Proposed plans were to increase the water use efficiency for improving the expected yield production by an average of 20%. Furthermore, the standardized groundwater index was decreased from 1.1 to –4.3 for winter crops. [ABSTRACT FROM AUTHOR]