pso algorithm

 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.

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).