pso algorithm

he role of auditors is to add credibility to financial information and reduce the risk of management manipulation or concealment. It is very important that auditors are independent of the company's management. In practice, however, different circumstances can pose a potential threat to the auditor's independence and, consequently, the quality of the audit. One of these threats is the employment of a previous auditor or the Employer's loyalty to the auditor. So far, relatively little research has been done in this area. Therefore, the present study for the first time optimized the level of audit quality in the face of customer loyalty by using the algorithm (PSO) with a sample of 87 companies listed on the Tehran Stock Exchange. The results showed that in each group of Employer loyalty and nonloyalty, no difference was observed in the level of audit quality. In fact, the use of particle aggregation algorithm in this study showed that audit quality has optimal coefficients in both groups and auditor independence, which is one of the basic components of audit performance is not affected by the decisions to appoint an auditor by management

The study of the effects of different strategies and beliefs of investors on stock prices has always been considered by researchers and market participants.The purpose of this article is Modeling the effects of behavioral biases of investors in Tehran Stock Exchange in heterogeneous agent model and and the Optimization of using PSO Algorithm.To do the research first we determine the crices point and the crash of stock exchange based on the trend of index changes, the market value and trades value. The fundamental price and the market price, 40 days before and after the crisis was determined.I also used PSO Algorithm to optimize the output data in simulated process and at the end comparing the simulated price with the real market data I analyzed the model ability to explain the investors behavior at the crisis.The results of the research showed that the negative market sentiment act as the most important Behavioral agent to explain the stock price behavior at Tehran Stock market crash.

Implementing the energy storage improves power load response, and network reliability, as well as reduces the need to build new power capacity in the electricity Using the energy storage improves responsiveness power into load, increases network reliability, and reduces the need to build new power capacity in the electricity industry. Regarding the economic- environmental benefits of using energy storage in the electricity industry, the main objective of this research is to investigate the application of electrical network’s energy storage with the aim of minimizing losses, environmental pollution, and system fuel costs. In this regard, three scenarios have been designed under the multi-objective particle swarm optimization (PSO) algorithm, which in scenario number 1, network consumption load is provided only by diesel generators. In scenario number 2, the renewable energy sources of wind and solar are added to the network, and in scenario number 3 further diesel generator and wind turbine and solar panels, energy storages are added to the network, and the PSO algorithm for optimal placement of the storage devices is performed. The results show that the most efficient result for the designed purposes can be achieved by solving the model under scenario number 3. Accordingly, the amount of network losses, fuel costs, and pollution in motion from the first scenario (base scenario) to the third scenario shows a decrease of 432 kW, 13.7 thousand dollars, and 75 kg, respectively. These results can help to optimum usage of energy storage devices in order to improve sustainability and network security, losses decreasing, and pollution decreasing in the electricity industry.

The role of accounting information and their features and characteristics have significantly increased in the past few decades. The reason of this fact is the behavior of the users in their decision making process. It is clear that every user wishes for quality information with low costs to reduce the information asymmetry and the possibility of a better decision make. In fact, this is the prerequisite of producing high quality accounting information. The current research measures the quality of accounting information in the Tehran Stock Exchange in an eight-year period (2009-2016), and then provides an optimization of its effect on sustainable and intolerable shareholders by using the PSO algorithm.The qualitative characteristics of accounting information in this research are the conformity of cost with income, timeliness of profit, conditional and unconditional conservatism, quality of disclosure, special choice, verification value, predictive value, honest expression, cautiousness, completeness, and comparability.

The vision of the Islamic Republic of Iran in the 1404 horizon illustrates a bright future for the country, a program that is more qualitative than a little. To achieve the objectives of the vision document, there is a need for a quantitative analysis of the indicators discussed in the document. To achieve this, economic exports as one of the major macroeconomic indicators have been simulated and predicted by Pso algorithms for Iran and Turkey. Using the bands pattern, using the four criteria of mean deviation (MSE), root mean square deviation (RMSE), absolute error percentage (MAPE) and absolute error (MAE) simulation have been performed. The results indicate that the gap between exports of  two countries has continued since 2000, with the current trend of Turkey's exports going to double the export of Iran's economy.

 Development of reliable models for estimation and prediction of changes inTotal Electron Content (TEC) of the ionosphere is still considered to be a real challenge for geodesists and geophysicists. This ispartly due to the nonlinear behavior of the physical and geophysical parameters affecting the TEC variations, as well as the difficulty in accurate measurement of some of these parameters. Due to its specific nature, as well as its physical and geophysical properties, quantity of TEC hasspatio-temporal variations, which can be attributable to daily, and seasonal variations, various anomalies, or periods of solar activity. Total Electron Content is the quantity which can be used to study ionospheric activities, as well as the spatio-temporal variations in electron density of this layer. In fact, TEC is the total number of free electrons in the path between the satellite and the receiver in a one square meter column. The measurement unit of TEC is TECU, which is equivalent to 1016electrons/m2. Due to inappropriate spatial distribution of GPS receivers and their limited number, as well as observationaldiscontinuity in the time domain, TEC values and electron density obtained from theGPS measurements will be spatiallyand temporallyconstrained. In order to calculate TEC value in areas lacking observation or appropriatestation distribution, TEC value obtained from GPS measurements must be interpolated or extrapolated in a suitable manner. 

 By combining wavelet localization features with standard neural networks, Wavelet Neural Networks (WNN) have emerged as a new mathematical method for modeling and predicting the behavior of different phenomena.In WNNs, the output parameter is usually calculated by the following equation: (1) wherex is the inputobservations vector, is a the multi-variablewavelet whichcan be calculated by the tensor productof m (basic function of single variable wavelets), ë is the number of neurons in the hiddenlayer, and ù shows the network weight. Unlike the Backpropagation (BP) algorithm, PSO is a global search algorithm that can optimize the initial weights and introduce the appropriate structure for the network. Equations used in this algorithm are as follows: (2) (3) In which, shows the initial weight, represents the particle’s velocity i in repetition t, c1 and c2, indicate the particle acceleration coefficients, is the current position of particle i in repetition t and gbest represents the best particle position. The present study took advantage of a smoothing algorithm to determine STEC observations. Observed STEC values are as follows: (4) To obtain TEC value along the zenith, the following mapping function can be used: (5) Which we will have: (6) Elev. in relation (6) is the satellite’s elevation angle. 

 Observations of 37 Iranian GeodynamicNetworkson 2012.08.11 (DAY 224) were used to evaluate the efficiency of WNN and PSO training algorithm in modeling and predictingspatio-temporal variations of TEC in Iran. Of the 37 stations, 5 were used as test stations, 2 were used to evaluate the wavelet neural network, and the rest were used to train the network. Four different combinations of input observations are examined in this paper. Number of input observations selected from the Iranian Permanent Geodynamic Network(IPGN) to train the WNN using PSO algorithm was25, 20, 15 and 10, respectively.Table 1 shows the characteristics of different combinations evaluated in this paper. Table 1. Characteristics of the observations used in the different combinationsevaluated To evaluate the accuracy of the results obtained from IRI and WNN model, all results were compared with TEC observations obtained from GPS. Table 2 shows the correlation coefficient for different scenarios. Table 2. correlation coefficient for different scenarios According to Table (2), the first scenario in WNN method with GPS hasthe highest correlation coefficient. Even when the number of observations in the databasedecreases in the third scenario, theWNN method still has a higher correlation coefficient compared to the IRI2012 model. In the fourth scenario, the correlation coefficient for WNN method is reduced to some degree. The average relative and absolute error values at the 5 test stations were calculated for the four different scenarios and presented in Table3. Table 3. Comparison of mean relative error and absolute error values at 5 test stations for four different scenarios. Statistical analysis of relative and absolute error showssuperiority of WNN method in TEC modeling as compared to the IRI2012.

 To model total electron content of the ionosphere, 4 combinations of observations were evaluated. 25, 20, 15 and 10 stations were used to train the wavelet neural network. 300, 240, 180, and 120 observations (latitude and longitude, observation time)were considered in the database, respectively.Results of the analysis indicated that with a decrease in the number of observations in the database, the absolute and relative error increase, while correlation coefficient decreases. This decrease was not evident before 180 observations, but relative and absolute errorreached up to twice their values with 120 observations. It should be noted that even with 120 observations (10 stations for training), results of the wavelet neural network model are more accurate than the results of the IRI2012 model.

Unmanned aircraft (UAV) refers to flying devices remotely or by internal direct and control the autopilot. They can be different types of accessories such as cameras, sensors and communications equipment to carry these birds are able to perform operations such as tracking aerial imaging from the field, tracking ground targets, target aircraft, electronic warfare, suicide and so on. One of the drones that are placed in multi-rotor VTOL and has six degrees of freedom. In this paper, first, dynamic modeling and then environmental effects are calculated on the bird; the fuzzy control and its implementation for the quadrotor are described; further, the pilot's structure is dealt with, and the simulink is designed from the quadrotor and the pilot's bird; and, finally, using the aggressive algorithm The route is designed in two different ways, indicating the result of a shorter period of time and an increase in the accuracy of the operations; hence, it can be an interesting model for future drone drills

In this paper, a context-aware system is proposed to offer places for visit to a group of tourists. The proposed system takes into account the factors of the tourist's interests as well as the travelled distance and seeks to maximize their satisfaction. To this end, clustering of individuals is performed according to their interests. Therefore, by using the PSO algorithm, the proposed system offers the best places for tourists during their stay. The simulation and implementation results of the proposed system show that tourists are more quantitatively satisfied in comparison with three previous methods for a case study of the city of Isfahan.
One of the ways for tourists to visit unfamiliar places is to use the tourist guide systems so that the they can get better suggestions. These systems often have a great deal of influence on the decisions they make by providing information about attractions that are relevant to the needs of the tourists (Samany, 2012).
Today, people are interested in group trips. Nonetheless, most of the tour-guide applications seek support for a tourist and do not include services for social tourism (Buriano, 2006). On the other hand, tourists expect that if they want to travel with a group of people they can use the technology available in the tour-guide systems (Groh, 2015). In this paper, the subject of group tourism has been studied and a system is proposed that can support group of tourists.
Context-aware systems provide intelligent services by knowing the information of user situation and environment. These systems are used to remove unnecessary information by doing some sort of content filtering (Pessemier, 2014). "The context refers to any kind of information that describes the situation of an entity. An entity can be considered as a person, a place, or any object that interacts with the system" (Dey, 2001). In many cases, different tourists have different needs and preferences, so the use of contextual information plays an important role in providing special offers to them (Adomavicius, 2016).
In group tours, there is a shared desire for people to visit a place. To meet this goal, the proposed system has been designed to cluster people according to their interests. Placing people with similar interests in a group will allow the satisfaction of all members of the group to be met with a proposal tailored to the interests of the group. For this purpose, a DBSCAN-based clustering algorithm has been proposed. The proposed algorithm clusters people based on their interest dispersion.
In each day, the proposed system offers seven places with the highest personal and group priority to each tourist group. Given that the number of tourist attractions in a city may be high, the PSO particle swarm algorithm, which is one of the evolutionary intelligence algorithms for large space search, has been used. Each particle represents a selection of tourist attractions. Finally, due to the lack of familiarity with the important places such as hospital, pharmacy or restaurant in the city, there is a mechanism to suggest at any moment the nearest restaurant, hospital or pharmacy.
Discussion and The implementation of the proposed system is based on the C # programming language and MySQL database. In order to evaluate the results, the proposed system is compared with the previous three methods. The comparison results show that the proposed system has greatly succeeded in satisfying the groups of tourists.
The proposed system has been instrumental in targeting the satisfaction of group of tourists through their categorization as well as the context-aware suggestions. The system could be extended by considering other types of context elements as well as specifying the groups with more details (e.g. Family, friendly or colleague's groups).

The defense sector is one of the key sectors of Iran that has devoted an important part of the total public spending. Any effort for forecasting the future defense expenditure trend is of special importance for policy-makers of defense sector. In this paper, we specified a defense expenditure model relying on theoretical basics in order to obtain desirable forecasts. On the basis of three forms of linear, exponential and quadratic equations and using theoretical foundations in the field of defense expenditure function, we used genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to simulate Iranians defense expenditure during 1959-2012. Then we selected the superior model in terms of prediction power criteria and forecast consumption until 2025. Based on the results obtained, it can be said that the defense cost function simulated by the PSO algorithm with exponential form is more compatible with the structure of Iran and therefore, it has been selected to predict expenditures from the sample until 2025. The predicted results also indicate that defense spending in Iran will increase with a relatively moderate slope by 2025.

Since one of the most important sources of information for investors and other beneficial is dividends forecast، this study tries to find models for predicting variables effective on dividend. To do this، information from chemical companies listed in Tehran Stock Exchange during the years 2006 to 2010 are used. The independent variables are accounting ratios and the dependent variable is dividend. The model framework is a combination of PSO-SVR and PSO-LARS algorithms. PSO algorithm identifies optimal combination of variables that influence the anticipated dividends. Then the data related to the variables selected by PSO are entered in to the SVR and LARS algorithms separately and train the algorithms. Then the algorithms are tested with evaluation data. Thus the prediction errors can be measured and the methods be compared. The research results show that combining PSO algorithm with LARS or SVR algorithm، as compared to using only SVR and LARS، can provide a better predict of considered affecting factors. Comparing the two combination methods، PSO-LARS and PSO-SVR، PSO-SVR shows that prediction error is less.

Exchange Rate uncertainty means an unpredictable change in the exchange rate that can cause a great impact on other economic variables and institutions such as banks and credit institutions especially in developing countries. In this study, using monthly data from the 2009/3- 2013/9, the effect of recent exchange rate crisis on Iranian banks and financial institutions is estimated and predicted. To do that, models such as VAR, VECM, Genetic and PSO Algorithm are employed. The results of estimating the VAR model indicates that there is a negative relationship between exchange rate volatility and stock market returns. Further, Co-integration test results imply the existence of an indirect long-run relationship between these two variables. By simulation of linearly and exponentially models using Genetic and PSO algorithms to compare the accuracy of these models with estimated VAR model, it was determined that VAR model can predict more accurately. Prediction results showed that persistence of exchange rate fluctuation can lead to first a diminishing increase followed by reduction in the index. However, reduction of exchange rate can lead to an increase in financial and credit institution's stock index.