imperialist competitive algorithm

The goal of any structural design process is to produce a safe design that meets all the design codes requirements, while trying to minimize the cost of the design. In Civil Engineering Structures, majorly cost is the objective of optimization of the structure. In most cases, the material cost is included in objective function. In some cases, construction cost is also included in objective function. The objective of the optimization is to minimize the cost of the Structures while satisfying strength and serviceability constraints. In order to investigate the efficiency and accuracy of the modern optimization methods in comparison with classical methods, an elevated INTZE tank is primarily designed based on the recommendations of Indian code and then optimized using Genetic Algorithm (GA), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO). objective function is the material cost of the tank which is the function of the design variables. The total material cost of the tank can be expressed as the sum of the cost of concrete, reinforcement and cost of formwork. Design constraints of the problem also include the stresses of the structure members and the limitations of the volume and geometric dimensions of the water tank. All constraints were satisfied and the stresses and stability constraints were in acceptable ranges.

Integrating large-scale wind farms with the main grid poses several challenges to operating the power utilities. One of the most important is to extract the maximum possible power in different operating conditions from the installed capacity. One of the common solutions in wind power plants equipped with doubly-feed induction generators is to control the output power of the rotor by using a proportional-integral controller.In this paper, a modified chaos-based imperialist competition algorithm is used to manage the control coefficients of twenty wind turbines (2 MW) located in a microgrid. The results show that the use of chaos theory improves the quality of convergence tracking of maximum power points during rapid changes in wind speed. In the classical proportional-integral controller, when the wind speed changes rapidly, the generated torque contains a large ripple and this controller is unable to overcome the nonlinear and ripple torque properties. This weakness increases the stress on the system and can damage the structural equipment of the generator. At the same time, the results obtained using the chaotic-based imperialist competition algorithm show that not only the ripple content is significantly reduced, but also more than 40% of its subsurface value is reduced.

Fog computing address numerous cloud computing challenges such as high latency, low capacity and network failure.  The cloud computing infrastructure includes a large number of IoT devices with the ability to process in the cloud environment.  In fog computing, processing and storage provide on IoT devices locally instead of remote servers, therefore, fog computing is the best choice to enable IoT in order to provide efficient, faster and secure services for many users on the edge of the network.  Fog computing have a variety of challenges. One of these important challenges is resource management and task scheduling such that solving this problem has a great impact on system efficiency and service quality. In this paper, we present a task scheduling approach based on the imperialist competition algorithm namely, Imperialist Competitive Algorithm-based Task Scheduling in Fog Computing (ICTF). In the proposed method, we consider the search space as a directional graph. Assume that each task that contains a set of tasks is a graph with a root node and an end leaf node whose middle nodes are the task set. Each path in this graph that starts at the root node and ends at the leaf is a solution represented by a string. This solution is modeled as a country. Therefore, in the proposed method, the concept of country includes the tasks of a job along with the fog nodes that are assigned to these tasks. The initial population consists of a random number of these solutions. ICTF presents a cost function consisting of three important criteria for assessing the initial population of countries and determining the imperialists and colonies countries include energy, execution time and execution cost. The assimilation operation is performed on two different members of countries, namely the imperialists and colonies country, and two new types of members are created called children. The countries participating in this process are among the best countries and are selected using the cost function. In this process, the best offspring produced are passed on to the next generation, and this operation continues until the final population of the countries is obtained. The assimilation operator has different models and in this article we use the two-point assimilation operator. The name of the revolution operator used in this algorithm is the inverse of the task. This operator randomly selects two tasks belonging to a fog node and moves them together. The above operation is repeated until the population converges and reaches the final answer. We show that our proposed approach is more efficient in terms of makespan, resource utilization, energy consumption and remaining energy compared to the similar approach.

Investing on the stock exchange, as one of the financial resources, has always been a favorite among many investors. Today, one of the areas, where the prediction is its particular importance issue, is financial area, especially stock exchanges. The main objective of the markets is the future trend prices prediction in order to adopt a suitable strategy for buying or selling. In general, an investor should be predicted the future status of the time, the amount and location of his assets in a way that increases the return on his assets. Stock price prediction is one of the most challenging topics in the field of forecasting, which has attracted many attentions from researchers. The various factors of the markets have caused the situation that they always have a dynamic and complex process. Therefore, researchers have been determined to look for new prediction methods of stock price, which will reduce the instability and complexity of the markets. In fact, the most of recent studies have shown that the stock market is a nonlinear, dynamic, and non-parametric system that is affected by various economic factors. The applications of artificial intelligence and machine learning techniques to identify the relationship between the factors and stock price exchanges can be organized in seven major groups such as neural networks and deep learning, support vector machine, decision tree and random forest, k nearest neighbor, regression, Bayesian networks and fuzzy inference-base methods. Due to the mentioned prediction methods have their own challenges, hydridizations of the meta-heuristic algorithms and the methods were applied to stock price prediction. In this paper, a new hybridization of Fuzzy Inference System and a novel modified Fuzzy Imperialist Competitive Algorithm (FICA+FIS) are proposed to stock price prediction. To achieve this aim, two Fuzzy Inference Systems are designed to tuing the ICA’s parameters based on three effective factors in search strategy and to predict stock price based on 10 effective economic factors. The candidate fuzzy rules set of the inference engine is obtained by the FICA for the second FIS and six fuzzy rules of the first FIS are designed based on the ICA’s behaviour. The FICA+FIS has 10 inputs of the stock price variables including the lowest stock price, the highest stock price, the initial stock price, the trading volume, the trading value, the first market index of the trading floor, the total market price index, the dollar exchange rate, the global price per ounce of gold, the global oil price, and its output is also the stock price. The inputs and output variables consist of three linguistic vairables such as Low, Medium, and High with triangular membership functions. Each country (search agent) of the FICA contains information on all the fuzzy rules of the inference engine attributed to the country and has r×12 elements, where r is the number of fuzzy rules. The FICA’s objective function is the mean square error (MSE) to evaluate the power of each country. A challenge of the ICA is the proper tuning paprameters such as the Revolution Probability (Prevolve), Assimilation Coefficient (Beta) and the Colonies Mean Cost Coefficient (zeta), which has a great impact on the efficiency of the algorithm (precision and time of access to solution). These parameters are usually constant and according to different problems, they have different values and are given experimentally. In this paper, the parameters are tuned based on the number of iterations that the best objective function value has not improved (UN), the number of imperialist (Ni) and the current number iteration (Iter). To this aim, a FIS is designed based on six fuzzy rules that UN, Ni and Iter are its input variables and Prevolve, Beta and zeta are its output variables. To analyze the efficiency of the FICA+FIS as a case study, six datasets are collocted from six companies which were active between 1389 to 1394 in Tehran Stock Exchange such as Pars Oil, Iran Khodro, Motogen, Ghadir, Tidewater and Mobarakeh. The information of around 2000 days are collected for each company and the data are divided to train and test data based on cross validation 10-fold. To compare the performance of the FICA+FIS, two groups of stock price prediction methods were implemented. In the first group, the fuzzy rules of the FIS’s engine to stock price prediction are obtained by the classic draft of the Imperialist Competitive Algorithm (ICA+FIS), the Genetic Algorithm (GA+FIS) and the Whale Optimization Algorithm (WOA+FIS), which are used to compare with the FICA. The second group includes classic stock price prediction methods such as multi-layered neural network (NN), support vector machine (SVM), CART decision tree (DT-CART), random forest (RF) and Gaussian process regression (GPR), which are used to compare with the FICA+FIS. The experimental results show that first, the improved fuzzy draft of the ICA performed better than its classic draft, the GA and the WOA, and second, the performance of the FICA FIS is better than other investigated algorithms in both training and testing phases, although the DT is a competitor in the training phase and the RF is a competitor in the test phase on some datasets.

Abstract- The aim of this study is to investigate writings to find out the mood of people in typing texts. In this study, 14640 tweets related to airlines were used to analyze emotions in three categories: positive, negative and neutral. The novel proposed approach has three main steps. In the first step, we perform a pre-processing operation to purify the dataset. In the second step, using the Imperialist Competitive Algorithm (ICA), the main keywords from all the existing texts are extracted. Keywords are the words that have the most impact on categorization. Then, a convolution neural network (CNN) is exploited to extract more features. In the last step, classification, using a multilayer perceptron neural network (MLP), is applied. In the proposed new method, unlike the conventional methods in which words go to the next stage after preprocessing, we use the Imperialist Competitive Algorithm to extract the main words from all these words, which in turn causes There is a significant reduction in the volume of input words.using this new proposed approach, we achieved precision, accuracy and recall of 0.990, 0.983 and 0.875, respectively. The experimental results indicated the superiority of the proposed method the comparison with other well-known approaches.

In this paper, the purpose is to improve the performance of the Imperialist Competitive Algorithm. Local search is added to the algorithm and this is done in two stages. In first stage, selection of the initial optimization parameters for starting the problem is done wisely and the population is leaded to optimal point. In second stage, after each 10 iterations a local search is performed between colonies and the imperialist of some of the strongest empires, in order to gain more accurate answers while not making a notable change in the process of reaching the answer. This method is not applied to all empires, so that calculations would not overflow. The proposed algorithm is tested using benchmark functions and based on the comparisons, the performance of the initial algorithm is much more improved when dimensions of the optimization problem goes higher. With few unknown parameters, conversion time and final minimum values are close in both methods, but by increasing the dimensions of the problem, even in some cases total calculation time is reduced to a half and the accuracy of minimum value is improved by 10 orders of magnitude. At the end, one application of the method is investigated in finding the suitable control gains for controlling a helicopter, which is a relatively a new application, and the performance is evaluated comparing to pole-placement and by state-feedback control.

management of these resources. The use of modern methods, including ANN and evolutionary algorithms in estimating water quality, due to its high speed, convergence and efficiency, saves and reduces costs and the best management. The main purpose of this study is to evaluate the results of the chemical analysis of groundwater samples from 14 wells in Jolfa plain and also estimate the ground water quality parameters using imperialist competitive algorithm (ICA) and ANN. Therefore, ground water quality parameters include TDS, EC and SAR estimate using imperialist competitive algorithm (ICA) and ANN and groundwater resources quality in terms of drinking, agriculture and industry were examined by Wilcox, Schuler and Piper and standards. Correlation coefficient of (R2) 90%, indicates the acceptable accuracy of ANN compared with ICA algorithm in estimating groundwater quality parameters. By using different diagrams the results show that the hardness of samples are too much and not suitable for drinking. It should also be noted that a very high hardness and corrosion of sample, water not be used in industry. The salinity of 7 samples is very high and according classification is located in C4S2 class and not suitable for agricultural consumption.