Measurement of magnitude and frequency of the voltage flicker is very important for monitoring and controlling voltage flicker efficiently to improve the network power quality. This paper presents two new methods for measurement of flicker signal parameters using Shuffled Frog Leaping Algorithm (SFLA) and Imperialist Competitive Algorithm ((ICA)). This paper estimates fundamental voltage and flicker magnitudes and frequencies with proposed methods. The goal is to minimize the error of the estimated magnitudes and frequencies via a designed fitness function. At first, we introduce voltage flicker and its measuring techniques. Then voltage flicker model is analyzed. At the next part, a review of SFLA and (ICA) is presented. These methods will be applied to a test voltage signal and the results are be analyzed.

Taking into account that the stability analysis of the earth slopes is a compl(ICA)ted geotechn(ICA)l problem and conventional methods of analyses because of circular slip surface assumption, are incapable to estimate the location of the slip surface especially in non-homogeneous earth slopes. Therefore, the new methods for the study of these types of slopes are necessary. Nowadays, the methods based on optimization principles are developed and the main point in the appl(ICA)tion of these new methods is the evaluation of the capability of these methods. Therefore, optimizing these problems needs robust Algorithms. In this research, three meta- heurestic Algorithms were applied for the slope stability analyzing of three studied and selected cases from literature. For all three cases of study, a non-circular slip surface is considered. Factor of safety was computed and compared with the same cases analysed analyt(ICA)lly. The obtained results ind(ICA)ted that (ICA) had the best performance and FA had the worst results for the cases studied in this research.

Optimization of haulage system in open pit mines is very important due to its high operating costs. Till now a large no. of researches have been conducted to optimize the truck fleet size in an open pit mine in dispatching and non-dispatching modes. These researches have gained many achievements in this regard and still they are going to continue. On the other hand, nowadays, appl(ICA)tion of artificial intelligence is spreading vastly to optimize systems in the field of (mining) engineering, especially in np-hard problems. One of the new meta-heuristic Algorithms in engineering field is Imperialistic Competition (IC), which is developed primarily for electr(ICA)l systems. Recently, it has found its appl(ICA)tion in the field of mining systems as well. In this paper, appl(ICA)tion of IC Algorithm for optimization of haulage system in open pit mines is introduced. Implementation of the IC Algorithm for Songon Copper mine was validated with comparison of its results with those of Genetic Algorithm. Comparison showed promising results. Optimization of the system with IC Algorithm improves the mine production from 32 thousand tons as the current situation to 37 thousand tons, while optimization of the system with Genetic Algorithm improves the mine production to 35 thousand tones.

In this paper, we present a new method to determine the optimal location and parameter settings of Unified Power Flow Controller (UPFC) for removing voltage violations and transmission lines overloading. UPFC is considered as the most powerful member of the FACTS devices, that it can control shunt and series power flow. This option gives to UPFC the power to control the voltage profile and transmission lines flow simultaneously. We used the Imperialistic Competitive Algorithm ((ICA)) to determine the optimal location and optimal parameter settings of UPFC to improve the performance of the power system specially removing voltage violations in the buses and solving transmission lines overloading to increase loadability in the power networks. This procedure is proposed to be applied on IEEE 14 bus system to show the validity of the method.

Lockstitch sewing machine is one of the most common apparel industrial machines. The thread take-up lever mechanism of a sewing machine has an important role for proper stitch forming and smooth working of the machine. Acquiring a mechanism with optimized geometry is of great importance to reach this goal. Therefore, in this study the optimization of the thread take-up lever mechanism with respect to some important features such as the optimal path of the coupler point and variation of coupler point acceleration (jerk) is presented. Modif(ICA)tion of the objective function with respect to the jerk is applied to assure smooth movement of the thread take-up lever during sewing process.For this purpose, the Imperialistic Competitive Algorithm is used to find the optimal link lengths of the take-up lever mechanism. The analysis results present further verify that the coupler point jerk in the optimized mechanism in the horizontal direction has been decreased about 52 percent without any conflict in the consistent operation of different parts of the sewing machine.

Rain is one of the most important climatic factors affecting human activities which has also an important role in the field of water resources management. This weather phenomenon is a complex atmospheric process, which is highly dependent on space and time and thus not easy to predict. The trends of change in rainfall with time is a non-stationary stochastic process with high uncertainty and it is subject to various random factors. There have been many attempts to find the most appropriate method for rainfall prediction using for example meteorolog(ICA)l or satellite data with a numer(ICA)l weather prediction model, or even applying several techniques such as the artificial neural network or fuzzy logic as a forecasting approach. Also some methods, such as the time sequence method, probability statistics method cannot fully reflect the characteristics of the rainfall phenomenon, and the prediction results cannot be satisfactory. In order to improve the accuracy of rainfall forecasts, it is necessary to use a new rainfall prediction model such as intelligent methods and meta-heuristic Algorithms. In this study, the “imperialist Competitive Algorithm” ((ICA) for brevity) and the (ICA) combined with the fuzzy logic Algorithm were used to evaluate and compare their performance and ability in forecasting the amount of daily rainfall in semi-arid climate of Kerman in the southeast of Iran. So, 30 years of daily data in Kerman’s synoptic station (1981–2010) and 10 years of daily data in Zarand and Rafsanjan’s synoptic stations (2001–2010) were used in the rainy season (7 months of the year). Therefore, based on the previous studies, five parameters including precipitation, wet temperature, dew point, relative humidity and cloudiness were used to forecast rainfall in futures days. Having surveyed the data, first the applied computer codes were written in Matlab 14. In the (ICA) with fuzzy logic, the (ICA) was used for determining the membership functions’ ranges and values of the weights instead of the trial and error usually used in appl(ICA)tion of the fuzzy logic. Three higher accurate outputs were identified for each station separately. Among these outputs, for each station, the best output was chosen and used for the final phase of optimization. Four more effective variables in Kerman’s station (precipitation, wet temperature, dew point, and cloudiness), two more effective variables in Rafsanjan’s station (precipitation and cloudiness) and three more effective variables in Zarand’s station (precipitation, wet temperature, and relative humidity) were identified after optimizing with five input variables. Results showed that the rainfall height’s prediction was accompanied with a signif(ICA)nt error based on the mentioned methods, so that the coefficients of determination (R2values) were obtained 0.54, 0.44 and 0.40 in, respectively, Kerman, Rafsanjan and Zarand’s synoptic stations. On the other hand, the forecast of the occurrence and non-occurrence of the rainfall with the (ICA) ind(ICA)ted reasonable results and in the best results 61.4%, 51.9% and 51.2% of days were predicted correctly in, respectively, Kerman, Rafsanjan and Zarand’s synoptic stations. The accuracy of calculations was improved with the (ICA) combined with the fuzzy logic. Accordingly, 89.63%, 82.31% and 74.12% of days were predicted correctly in, respectively, Kerman, Rafsanjan and Zarand’s synoptic stations. The results of evaluating the performance showed that the (ICA) can produce a relatively appropriate simulation of the occurrence and non-occurrence of rainfall in future days, but falls short of ability to simulate the rainfall height properly. On the other hand, the combined (ICA) and fuzzy logic Algorithm provides a better simulation of problems involving high uncertainty.

Bankruptcy prediction is a major issue in classif(ICA)tion of companies. Since bankruptcy is extremely costly, investors, owners, managers, creditors, and government agencies are interested in evaluating the financial status of companies. This study tried to predict bankruptcy among companies registered in Tehran Stock Exchange (Iran) by designing imperialist Competitive Algorithm and genetic Algorithm models. It then compared the accuracy of the two models in financial conditions of Iran and sought the best model to predict company bankruptcy one, two, and three years before its incidence. Also uses a model to surveying the financial position and also the subject of continuing operations about them to improve the quality of decision taken by shareholders and stakeholders. The study sample consisted of 38 bankrupt and 38 non-bankrupts companies during 2007-2016. The final variables used in both Algorithms were five financial ratios. The results showed that the imperialist Competitive Algorithm had better accuracy than the genetic Algorithm in bankruptcy prediction at the mentioned intervals.

Water scarcity, especially in Iran and during the recent droughts, emphasizes the importance of achieving an optimal operation policy for large dam reservoirs. In the last two decades, the annual optimization of dam reservoirs under controlled conditions, as well as climatic and real conditions, has attracted many researchers and experts. This study proposes a new approach to predict reservoir dam storage. The imperialist Competitive Algorithm ((ICA)) is a new approach in the field of evolutionary computation that calculates an optimal solution for different optimization problems. Using mathemat(ICA)l modeling of the social-psycholog(ICA)l evolution process, (ICA) provides a new approach to solve mathemat(ICA)l optimization problems, and compared to other Algorithms, it has appropriate speed and high convergence rate in finding an optimal answer. This research used the (ICA) for the annual optimization of the Kahir reservoir to derive optimal policies. Objective function downstream water issue needs to establish relationships based on continuity were selected. Comparison of (ICA) model in population 100 showed that the (ICA) Algorithm with average best objective function value of 125, 114. 6, and 85. 60 with a number of further evaluations of the objective function to achieve higher capacity is the optimum answer. The results showed a 6. 1 percent error in the implementation of the (ICA) Algorithm between the observed and predicted storages. The results of applying the (ICA) to the annual optimization problem demonstrate the capability of the proposed method.