In this study, two forms of non-linear energy demand functions are simulated to forecast the future situation of energy demand in Iran (total final energy consumption) by using trend lines of macroeconomic indices. In other words, we study how efficiency of the energy demand estimation in Iran can be improved through use of Particle Swarm Optimization ((PSO)) Algorithm. The (PSO)-based energy demand Simulation ((PSO)EDS) model is developed using population gross domestic product and export of goods and services as variables. In this essay, one of the suggested equations is exponential and the other one is quadratic. In energy demand projection, the quadratic form provides better results and can be used for evaluation of energy sector projects in Iran with greater effectiveness.

The nature of business and investment activities is such that earning a return requires risk tolerance. Choosing a stock portfolio is a difficult and difficult task that the investor sees in the face of the many and varied choices that she must choose as one of the best methods. The present study deals with the problem of stock portfolio Optimization according to the Value at Risk based intelligent fireworks Algorithm and compares it with Particle Swarm Optimization Algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic Algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used. A comparison was also made between the models by Lopez test. The execution time of the Particle Swarm Optimization was less than that of the fireworks Algorithm at all three levels of confidence, but the convergence speed of the fireworks Algorithm was faster than that of the Particle Swarm Optimization at all levels. Findings showed that the Value at Risk model using the fireworks Algorithm, despite the longer execution time due to better convergence speed and higher rank of Lopez test has a more appropriate validity for stock portfolio Optimization.

Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when comparing different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years-company during 2011-2016 (a sixyear period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based Algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, Chaid rule-based Algorithm has a good ability to predict stock prices.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

The analysis of steady-state flow within rockfill materials is performed using two methods of gradually varied flow theory that analyzes the flow as one-dimensional and the Parkin equation that analyzes the flow as two-dimensional. The calculation of the hydraulic gradient (i) is of great importance in both methods. Most of the research in this area has been done in homogeneous rockfill materials and limited studies have been carried out on the flow in heterogeneous rockfill materials. In this study, we used the results of experiments in homogeneous and horizontal heterogeneous rockfill materials with three layers of aggregates with large, medium and small size. In the experimental data used in the present study, changes in hydraulic gradient relative to the flow velocity in each of the three homogeneous layers were available separately and also the changes in the horizontal heterogeneous media consisting of three layers were available separately. Due to the fact that in order to form a horizontal heterogeneous rockfill media, large, medium, and small homogeneous layers are placed on top of each other, respectively, in the approach presented in the present study, the data of the steady flow of homogeneous media are placed in the same order in the Particle Swarm Optimization ((PSO)) Algorithm to optimize the coefficients of the binomial equation (a, b) and consequently the calculation of hydraulic gradient. In other words, in the present study, the values of the coefficients a, b, equivalent to the horizontal heterogeneous media, depending on how the homogeneous layers are placed on top of each other and only using the data of steady flow in the homogeneous media (large, medium and small size) Optimized. The results show that the difference of mean relative error (MRE) the approach presented in the present study than conditions where flow data in a horizontal heterogeneous media are directly used to calculate the coefficients a and b and consequently the hydraulic gradient, is 1. 38 percent.

Oil production Optimization is one of the main targets of reservoir management. Smart well technology gives ability of real time oil production Optimization. Although this technology has many advantages; optimum adjustment or sizing of corresponding valves is its issue. In this research optimum sizing of ICDs which are passive control valves has been focused on by designing a neural network to simulate reservoir behavior and applying Particle Swarm Optimization (PSW) Algorithm to find optimum port size for ICDs. Indeed; this work eliminates the need for lots of expensive and time consuming iteration through reservoir simulator. The achieved objectives of the work were oil production maximization and water production minimization.

This paper presents a suitable and quick way to choose earthquake records in non-linear dynamic analysis using Optimization methods. In addition, these earthquake records are scaled. Therefore, structural responses of three different soil-frame models were examined, the change in maximum displacement of roof was analyzed and the damage index of whole structures was measured. The soil classification of project location was divided into 4 different types according to the velocity of shear waves in the Iranian Code for Seismic Design. As a result, 8 frame models were considered. The selection and scaling were carried out in 2 stages. In the first stage, the matching with design spectrum was carried out using genetic Algorithm in order to achieve the mean of structural response. In the second stage, the matching with average of structural responses were carried out using (PSO) to achieve 1 or 3 accelerograms with related factors in order to be used in structural analysis.