Exploitation of dam reservoirs is one of the major problems in the management of water resources. In this reSearch, Crow Search Algorithm (CSA) was used for the first time to manage the operation of reservoirs. Also, the results related to the exploitation of the single-reservoir system of Shahid-Rajaei dam, located in Mazandaran province, northern Iran, which meets the downstream water demands, were compared to those obtained by applying the Particle Swarm and Genetic Algorithms. Time reliability, volume reliability, vulnerability and reversibility indices, and a Multicriteria decision-making model were used to select the best Algorithm. The results showed that the CSA obtained results close to the problem’ s absolute optimal response, such that the average responses in the Crow, Particle Swarm and Genetic Algorithms were 99, 75 and 61 percent of the absolute optimal response, respectively. Besides, except for the time reliability index, the CSA had a better performance in the rest of the indices, as compared to Particle Swarm and Genetic Algorithms. The coefficient of variation of the obtained responses by CSA was 14 and 16 times smaller than the Genetic and Particle Swarm Algorithms, respectively. The Multi-criteria decision-making model revealed that the CSA was ranked first, as compared to the other two Algorithms, in the Shahid-Rajaei Reservoir's operation problem.

Installing new energy sources as redundant black-start (BS) Units is an efficient way to enhance the speed of power system restoration, especially when there is a high risk that the available power plants considered as BS Units fail to operate. In this regard, this paper provides a new optimal design for the placement of the Gas Turbine (GT) as the redundant energy source to improve the power system performance during both restoration and normal conditions. In doing so, there will be contradictory objective functions to be minimized. Therefore, a Multi-objective problem (MOP), as a mixed integer linear programming (MILP), is defined. The Pareto optimal solutions of the MOP are obtained by using a new population-based meta-heuristic technique, called Crow Search Algorithm (CSA). Two power systems are used for the validation of the proposed method. The simulation results show that the system can benefit from this method not only to increase the capability of black-start generation, but also to improve the power system performance in normal conditions. During the restoration process, it also provides the optimal start-up sequences of non-black-start (NBS) Units with the optimal transmission paths.

Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or Multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary Algorithm is usually beneficial. In this paper, we proposed a hybrid method based on employing CSA and Otsu for Multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary Algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/ improved results both in time and uniformity.

Advanced Metering Infrastructure (AMI) is an essential segment of the smart grids that is responsible for gathering, measuring and analyzing the electricity demand. Energy losses in the electricity distribution and transmission network and electricity theft detection are major challenges of electricity suppliers around the world. The analysis of consumption data related to the customers is one of the essential resources to identify electricity thieves. In this paper, the Crow Search Algorithm (CSA) is improved and the factors weight (𝑤 ) and awareness probability (𝐴 𝑃 ) are obtained dynamically and used to adjust the parameters 𝐶 and 𝛾 related to the Support Vector Machine (SVM). The results illustrate that the ICSA-SVM framework has acceptable performance and detects fraudulent customers with high accuracy.

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for Searching the required data, particularly text documents. This is further facilitated by using Artificial Intelligence (AI) and optimization Algorithms which are highly potential in Feature Selection (FS) and words extraction. In this paper Crow Search Algorithm (CSA) is used for FS and K-Nearest Neighbor (KNN) for classification. Additionally, TF technique is proposed for counting words and calculating the words’ frequency. Analysis is performed on Reuters-21578, Webkb and Cade 12 datasets. The results indicate that the proposed model is more accurate in classification than KNN model and, show greater F-Measure compared to KNN and C4.5. Moreover, by using FS, the proposed model promotes classification accuracy by %27, compared to KNN.

By incremental deployment of renewable energy sources on microgrid frameworks, new technical and economic issues have emerged in the power system industry. The optimal operation of microgrids in the presence of intermittent renewable sources has been counted as a new challenge within the last decade. Microgrids are off-grid or grid-connected power systems on a very small scale encompassing different types of distributed generation sources and local loads. Generally, in isolated microgrids, the demanded energy of consumers is maintained by hybrid models of internal energy sources. The principal purpose of hybrid systems is to supply the electrical power demanded by consumers instantaneously as well as storing surplus energy for critical conditions. In this paper, a techno-economic and environmental base approach for optimal energy management of microgrids using Crow Search Algorithm is presented. Under study microgrid include renewable energy resources, battery and diesel generator as backup power generator. Annual cost and the released emission are considered as the objective function of the proposed method. The Crow Search Algorithm calculates power dispatch scheduling among generation Units. Simulation results of the proposed method show the appropriate configuration of the hybrid system that lead to decrease the annual cost of the system and the released emission.

Multi-label classification aims at assigning more than one label to each instance. Many real-world Multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence Algorithm based on the binary Algorithm of particle swarm optimization and a novel local Search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local Search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization Algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed Algorithm compared to other Algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of Multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

In this paper, the design of a hybrid renewable energy PV/wind/battery system is proposed for improving the load supply reliability over a study horizon considering the Net Present Cost (NPC) as the objective function to minimize. The NPC includes the costs related to the investment, replacement, operation, and maintenance of the hybrid system. The considered reliability index is the deficit power-hourly interruption probability of the load demand. The decision variables are the number of PV panels, wind turbines and batteries, capacity of transferred power by inverter, angle of PV panels, and wind tower height. To solve the optimization problem, a new Algorithm named improved Crow Search Algorithm (ICSA) is proposed. The design of the system is done for Zanjan city, Iran based on real data of solar radiation and wind speed of this area. The performance of the proposed ICSA is compared with Crow Search Algorithm (CSA) and particle swarm optimization methods in different combinations of system. This comparison shows that the proposed ICSA Algorithm has better performance than other methods.

The application of distributed generation resources and capacitor banks is increasing due to the distribution networks extension and also power demand growth. Determining the installation location and the capacity are two important and effective factors on the network power loss and the network performance improvement. If connected in the right place to the power network, distributed generation power plants and the capacitors have different effects such as loss reduction, voltage profile improvement, and network reliability augmentation. In this paper, in order to reduce the distribution system loss, simultaneous optimal placement of the distributed generation resources and capacitors in radial distribution systems is studied. Crow Search Algorithm is applied for this purpose. This Algorithm works on this idea that the Crows chase each other in order to find other Crows' food hiding place. The simulation is done on IEEE-33 & IEEE-69 buses network in MATLAB software. The simulation results demonstrate the efficiency of Crow Search Algorithm, comparing to other applied optimization Algorithms, in the problem of simultaneous optimal placement of the distributed generation resources and capacitors in radial distribution systems to reduce loss and enhance voltage profile.