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.

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.

Nowadays, the speed at which municipalities provide urban services and collect urban waste play an important role in improving the efficiency of this organization, and therefore, the satisfaction of the citizens. In recent decades, due to the dominance of consumerism culture in Third World countries, especially Iran, we witness an increase in the urban waste each and every single day. In spite of the modernization of waste collection machines, the service delivery speed has been neglected although it has always had a signif(ICA)nt impact on cost reduction and quality of service delivery. The city of Ardabil is no exception to this. It has four districts and 100 large and small neighborhoods in total that have always encountered the municipality with a major problem in terms of the rate at which urban wastes was collected and thus provided a beautiful outlook of the city. This article aims at adding the speed as a factor to the waste collection units by proposing the best route for the machines via Travelling Salesman Problem approach and IMPERIALIST COMPETITIVE ALGORITHM in MATLAB environment. Using the appropriate programming and defining those 100 neighborhoods for the model, the most optimal routes for the municipality’, s service units are introduced provided that the service units pass each neighborhood once and at the end return to the starting point again. The results of the study showed that the ALGORITHM used in this research for 100 neighborhoods in those four districts can provide the optimal solution with the repetition of 200 and respectively with the values of 99, 91, 93, and 97 and within the intervals of 30, 22, 30, and 24 seconds.

Background and Objectives: Optimizing the operation of reservoirs is the most important issue in water science and engineering which has been resolved through a variety of traditional optimization methods. One of these methods is optimizing using the meta-heuristic ALGORITHMs.The researchers around the world show the performance of meta-heuristic ALGORITHMs performance has been far better than procedures such as linear programming. The aim of this study is to evaluate the performance of IMPERIALIST COMPETITIVE ALGORITHM ((ICA)) and ant colony ALGORITHM (ACO) with appl(ICA)tion of chain constraints.Materials and Methods: In this research, (ICA), is used to solve the optimization problem of reservoirs operation. The approach taken in this research is appl(ICA)tion of relationship continuity in determining the initial position of countries, which has been proposed as a chain constraints.The results of applying the chain constraints and lack of appl(ICA)tion of the chain constraints have been compared and consequently these results have been compared with one of well -known ALGORITHM named as ant colony ALGORITHM.Results: The results ind(ICA)ted that IMPERIALIST COMPETITIVE ALGORITHM without considering the continuity equation, was rarely able to find possible answer and applying the chain constraints to determine the initial position of countries, enhanced the performance of ALGORITHM more efficiently and it leads to even better performance compared to ant colony ALGORITHM and find an appropriate value for the objective function, so that after running ten times, the mean for objective function for IMPERIALIST COMPETITIVE ALGORITHM was 15.822 and for ant colony ALGORITHM was 48.008.Conclusion: Result have shown with chain constraints the (ICA) and ACO ALGORITHMs have been unable to find feasible solution and their performance is fairly high. Therefore the chain constraints can be applied as an essential element in enhancing the efficiency of these ALGORITHMs. Also the results of the comparison between the ALGORITHMs has shown that (ICA) ALGORITHM performance has been far better than ACO ALGORITHM.

IMPERIALIST COMPETITIVE ALGORITHM ((ICA)) is considered as prime meta-heuristic ALGORITHM to find the general optimal solution in optimization problems. This paper presents a use of (ICA) for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the (ICA)، at run time، the suggested method (AC(ICA)) finds the optimum number of clusters while optimal clustering of the data simultaneously. To increase the accuracy and speed of convergence، the structure of (ICA) changes. The proposed ALGORITHM requires no background knowledge to classify the data. In addition، the proposed method is more accurate in comparison with other clustering methods based on evolutionary ALGORITHMs. DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method، the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering ALGORITHMs based on evolutionary ALGORITHMs.

The main enabling technology for cloud computing is the use of virtual machines. After making the decision of their placement on hosts, they will be set to run. This process is called virtual machine placement. This process has a great importance on energy consumption and resource wastage avoidance. On the other hand, the growing complexity of cloud infrastructure compounds the problem. In this article, the problem of virtual machine placement is transformed to an optimization problem. The goal is minimizing energy consumption and maximizing the profit of placement, simultaneously. A newly emerged optimization method, called IMPERIALIST COMPETITIVE ALGORITHM is applied in this paper. In addition, a unique method for generating new solutions based on already discovered ones, proposed. Finally the success of the proposed ALGORITHM is confirmed by simulation results and its evaluation is compared with GGA and FFD ALGORITHM.

In this study, a new approach is introduced to solve Blasius differential equation using of IMPERIALIST COMPETITIVE ALGORITHM ((ICA)). This ALGORITHM is inspired by competition mechanism among IMPERIALISTs and colonies and has demonstrated excellent capabilities such as simplicity, accuracy, faster convergence and better global optimum achievement in contrast to other evolutionary ALGORITHMs. The obtained results have been compared with the exact solution of Blasius equation and another result obtained in previous works and show higher accuracy and less computational requirements. In addition, the method presented with details can beeasily extended to solve a wide range of nonlinear problems.

IMPERIALIST COMPETITIVE ALGORITHM ((ICA)) is one of the recent meta-heuristic ALGORITHMs proposed to solve optimization problems. The IMPERIALIST COMPETITIVE ALGORITHM is based on a socio-polit(ICA)lly inspired optimization strategy. This paper presents four different variants of this ALGORITHM. These methods are applied to some engineering design problems and a comparison is made among the results of these ALGORITHMs and other meta-heuristics. The results show the efficiency and capabilities of the (ICA) in finding the optimum design.