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Background: Prediction of future climate change is based on output of global climate models (GCMs). However, because of coarse spatial resolution of GCMs (tens to hundreds of kilometers), there is a need to convert GCM outputs into local meteorological and hydrological variables using a downscaling approach. Downscaling technique is a method of converting the coarse spatial resolution of GCM outputs at the regional or local scale. This study proposed a novel hybrid downscaling method based on artificial neural network (ANN) and particle swarm optimization (PSO) algorithm. Materials and Methods: Downscaling technique is implemented to assess the effect of climate change on a basin. The current study aims to explore a hybrid model to downscale monthly precipitation in the Minab basin, Iran. The model was proposed to downscale large scale climatic variables, based on a feed-forward ANN optimized by PSO. This optimization algorithm was employed to decide the initial weights of the neural network. The National Center for Environmental Prediction and National Centre for Atmospheric Research reanalysis datasets were utilized to select the potential predictors. The performance of the artificial neural network-particle swarm optimization model was compared with artificial neural network model which is trained by Levenberg–Marquardt (LM) algorithm. The reliability of the models were evaluated by using root mean square error and coefficient of determination (R²). Results: The results showed the robustness and reliability of the ANN-PSO model for predicting the precipitation which it performed better than the ANN-LM. It was concluded that ANN-PSO is a better technique for statistically downscaling GCM outputs to monthly precipitation than ANN-LM. Discussion and Conclusions: This method can be employed effectively to downscale large-scale climatic variables to monthly precipitation at station scale.

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This research addresses the issue of balancing time-cost-income of talent management and succession in knowledge-based organizations. There are different approaches for attracting talents from outside of an organization to fill this gap. Although many different researches made clear insights about the importance of successful talent succession, only a few quantitative methods have been developed to deal with such problem. This paper by assuming such realistic assumptions, proposed a bi-level linear mathematical model, based on game theory approach. The performance of developed model has been assessed using PSO algorithm by gathering ten-year realistic data from an Iranian telecom company. The results show acceptable adoption to reality, based on realistic events.

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This research aims to describe a novel model, namely Hybrid Adaptive-Neuro Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO), for predicting corrosion rate of 3C steel considering different marine environment factors. In the present research, five parameters (temperature, dissolved oxygen, salinity, pH, and oxidation–reduction potential) were used as input variables, with corrosion rate being the only output variable. In the proposed hybrid ANFIS-PSO model, the PSO served as a tool to automatically search for and update optimal parameters for the ANFIS, so as to improve generalizability of the model. Eeffectiveness of the hybrid model was then compared those to two other models, namely Adaptive-Neuro Fuzzy Inference System–Genetic Algorithm (ANFIS-GA) and Support Vector Regression (SVR) models, by evaluating their results against the same experimental data. The results showed that the proposed hybrid model tends to produce a lower prediction error than those of ANFIS-GA and SVR with the same training and testing datasets. Indeed, the hybrid ANFIS-PSO model provides engineers with an applicable and reliable tool to conduct real-time corrosion prediction of 3C steel considering different marine environment factors.

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In this paper, an optimal Fractional-order Proportional-Integral-Derivative (FOPID) controller is proposed to control an offshore 5MW wind turbine’s pitch angle in above rated speed. The proposed pitch controller regulates the generator angular speed and consequently the generator power to its nominal value without any knowledge of the model. In order to find the parameters of the controller, a hybrid cost function is proposed, which consists of sum of absolute error signal and absolute rate of control signal in three different wind speeds. The wind speeds are chosen in the beginning, middle and at the end of the interval, thus, the optimized controller is able to show an acceptable performance in whole range of wind speeds, without any demand to nonlinear and complex controllers. To this end, the proposed cost function is minimized using three optimization algorithms: Differential Evolution (DE), Firefly algorithm and Particle Swarm Optimization (PSO). In order to evaluate the robustness of proposed FOPID, numerous wind profiles with different speeds and fluctuations are applied and the results are compared with the optimal integer order PID controller. The comparison demonstrates that the proposed FOPID has more effective performance and robustness than optimal integer order PID.