crow search algorithm

Choosing the right drilling and blasting methods is very important to improve mine productivity and increase profits, while increasing the safety of workers and the environment. One of the main goals in Blasting is to estimate the optimal amount of specific cost, which is described as the specific cost required for optimizing rock crushing, air blast, ground shaking, and rock throwing. In this Paper, to further improve the effect of blasting and increase the efficiency of mine production, we optimized the plan by considering the implementation of the blasting plan with respect to the drilling intervals, and a numerical simulation model was created to provide technical guidance for the optimization plan. In this Paper, data collection and analysis were done by the software and it was determined that with R^2=0.969, VAF=9.825, the lowest error is RMSE=0.031 and MAPE=0.323 with unit m^3kg/ . The polynomial function model with incorrect coefficients has a more satisfactory performance and this function model was subjected to sensitivity analysis using the cosine field method. The evaluation and analysis in this way also showed that the selected function model has a more accurate calculation of the specific cost. This proposed function was called in the bat algorithm and the crow search algorithm in MATLAB software, and the optimization operation was performed by the algorithms. In this Paper, the number of 11 data from 31 blasting series that were actually carried out in Sadat iron ore mine of Zarand series was collected. The special cost of this mine was modeled with 6 functions by spss software, and among them, the polynomial function model with incorrect coefficients was chosen to predict the special cost. The special cost optimization operation was performed by two bat algorithms and the crow search algorithm, and the special cost optimized by the algorithms was compared. By sensitivity analysis, the sensitive and effective parameters taken in this mine were identified for special spending. This analysis showed that all the input parameters used in this modeling have a strong sensitivity to the specific cost, and among them, the ratio of the height of the step to the thickness of the bar rock has the highest sensitivity and the total consumption cost has the lowest sensitivity.

Haptic Devices (HDs) are used to simulate virtual objects for the user. The dynamics of the HD and the opertator’s hand have a great effect on the stability and performance of the HD. In this research, the dynamic identification of the user's hand and HD has been done using methods of Artificial Bee Colony Optimization (ABCO), Crow Search Algorithm (CSA), and Genetic Algorithm (GA), during experiments in two cases. In the first case, only the dynamics of the HD has been identified, then in the second case, the mentioned methods have been generalized for the simultaneous recognition of the dynamics of the user's hand and the HD. For this purpose, first, in each case, the theoretical stability boundary is obtained as a function of the available dynamic parameters. Then, by performing experiments on the KUKA Light Weight 4 robot in two cases of presence and absence of the operator’s hand, the experimental stability boundary has been obtained. Afterwards, by using ABCO, CSA, and GA in each case the desired dynamic parameters are obtained in such a way that the difference between the theoretical and experimental stability boundary is minimized. Studies show that while all three methods have been able to identify the parameters well, the GA method produces less error and has a better performance in this field.

This paper presents the system stability-constrianted optimal protection coordination (SSCOPC) as stochastic problem in the microgrid with grid-connected and islanded operation modes, including renewable energy sources (RESs) and energy storage systems (ESSs). To regulate optimally the dual setting overcurrent relays (DSORs) and to selectin the reactance size of fault current limiter (FCL), the system minimizes the total relays operation time in the primary and backup protection modes. Also, it is subject to the limits of the coordination time interval (CTI), DSORs setting parameters, FCL size, and microgrid stability in the fault condition. Generally, the microgrid includes RESs and ESSs; thus, the proposed SSCOPC needs a daily operation of microgrid as stochastic model to calculate network variables before fault occurrence. Hence, s this situation minimizes the microgrid operation cost and load shedding cost of islanded microgid which are subjected to optimal power flow equations in the presence of RESs and ESSswhile considering the uncertainty of the load and its generation. Therefore, by applying the proposed problem on the 9 and 32 bus microgrid, solving the problem by the Crow Search Algorithm (CSA), and selecting optimal size of RESs and ESSs, based on operation and protection coordination indices, the proposed SSCOPC can obtain a fast protection solution that considers system stability in faulty conditions.

The utilization of concrete Incorporating with fibers is one of the proper issues of construction industry in last years. The main focus of this research to design a high performance self-compacted fiber reinforced concrete (SCFRC) by using an evolutionary algorithm, which is implemented in MATLAB. Crow Search Algorithm (CSA) and Genetic Algorithm (GA) are statistical ways which are developed by optimization based meta-heuristic solutions. A total of 67 concrete mixtures were considered by varying the levels of key factors affecting concrete strength of concrete, namely, water content (137.2-195 kg/m3), cement content (325.5-520 kg/m3), coarse aggregate content (722-920 kg/m3), fine aggregate content (804.9-960 kg/m3), nano silica content (0-49.6 kg/m3),percentage of volumetric of fibers (0-0.9 %), lime stone powder content (0-288.9 kg/m3) and superplasticizer content (1.75-10.5 kg/m3) were developed to design optimized mixture proportions. The objective function called maximizing concrete strength was formulated as an optimization problem on the basis of Multiple Linear Regression (MLR) method. The constrains including ratio of mixture proportions and absolute volume of mixture design were utilized to obtain an optimal-strength and cost-effective design. The concrete technological constraints were identified as the factors of experimental design for concrete production. The evolutionary implementation of results reached incorporating mixture proportions having strengths in range of 30 - 88.7 MPa. Five numerical examples for optimum mixture design of SCFRC were considered to evaluate the capability and efficiency of CSA and GA algorithm. These results were compared and concluded that CSA (3.38-14.49 % of mean error) performed better than GA (7.95-15.52 % of mean error) for this application. Also, the proposed evolutionary CSA and GA algorithms are found to be reliable and robustness tools to solve and optimize engineering and concrete technological problem.

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.

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.

In this paper, a framework for multi-objective optimization of a hybrid photovoltaic/diesel generator (PV/DG) system has been presented. This study follows the design of the PV/DG system at the lowest cost, lowest emission and highest reliability.The total net present cost (TNPC), CO2 emissions and the loss of power supply probability (LPSP) are selected asthe problem objectives. This system has been designed in three scenarios: 1- without considering uncertainty and operating reserve, 2- with considering uncertainties of the system and 3- with considering uncertainties of the system and using DG as operating reserve. In order to effectively solve this problem, multi-objective versionof crow search algorithm (MO-CSA) has been developed and the results are compared with the results obtained by well-known non-dominated sorting genetic algorithm II (NSGA-II). The coding of the problem has been done in MATLAB environment and the Pareto fronts have been compared. Simulation results indicate that combination of DG and PV is a promising alternative for supplying the electrical demand of stand-alone areas. Moreover, the results obtained by MO-CSA outperform the results obtained by NSGA-II.

The need for more efficient power systems has prompted the use of a new technologies includes Flexible AC transmission system (FACTS) devices. FACTS devices provides new opportunity for controlling the line power flow and minimizing losses while maintaining the bus voltages within a permissible limit. In this thesis a new method is proposed for optimal placement and sizing of Thyristor controlled series compensator (TCSC) and Static VAR compensator (SVC) for improvement power system operation using crow search algorithm. Optimal place for installation of TCSC and SVC is determined based on line’s reactive power flow and voltage collapse proximity indication (VCPI), respectively. Optimal size of FACTS devices as long as reactive power of generating units and optimal tap of transformers is computed by solving an optimization problem. The objective function is defined as minimizing the sum of the network costs includes costs of energy losses and costs of FACTS devices. Crow search algorithm is a novel metaheuristic optimization method that inspired from the intelligent behavior of crows in storing and retrieving foods. That’s this thesis is used for the first time in optimal placement and sizing of TCSC and SVC. The proposed method is simulated on IEEE 30 bus and 57 bus sample networks in MATLAB environment. Simulation results shows that crow search algorithm converges to optimal solution while meets all technical constraints.And Improves the power system efficiency.