grey wolf algorithm

This study focuses on optimizing the Zayandehrud Dam reservoir using two evolutionary algorithms—Grey Wolf Algorithm (GWA) and Particle Swarm Optimization (PSO)—to maximize reliability and minimize vulnerability in water resource management. Climate data, including temperature and precipitation, are extracted for the baseline period (1981–2010) and future intervals (2040–2069 and 2070–2099) using outputs from the HadCM3 model under two emission scenarios, A2 and B2. The impacts of climate change on water resources are assessed using the Artificial Neural Network (ANN) and IHACRES models. Additionally, the CROPWAT model analyzes climate change effects on water consumption and estimates irrigation demands.

This study presents a comprehensive approach to optimizing the Zayandehrud Dam reservoir using single-objective evolutionary algorithms—particle Swarm Optimization (PSO) and the Grey Wolf Algorithm (GWA). The research focuses on maximizing reliability and minimizing vulnerability in reservoir operations while accounting for climate change effects. The study area, the Zayandehrud River Basin, is the first defined basin in the Central Plateau of Iran.Climate scenarios for temperature and precipitation are extracted from the HadCM3 model for the baseline period (1981–2010) and future intervals (2040–2069 and 2070–2099) under two emission scenarios, A2 and B2. Using the spatial coordinates of the meteorological station under investigation, the time series of temperature and precipitation variables corresponding to the HadCM3 computational grid cell are retrieved.To downscale climate variables, temperature and precipitation changes in future periods are calculated relative to the baseline and applied to observed data. Future climate variables are estimated by adding temperature scenarios to observed temperature values and multiplying precipitation scenarios with observed precipitation records.Future runoff simulations are conducted using the IHACRES and ANN models. The estimation of water demand and consumption volumes is performed using the CROPWAT model. Subsequently, reservoir optimization is carried out utilizing PSO and GWA, with reliability maximization and vulnerability minimization considered as objective functions.

This study investigates the effects of climate change on rainfall, temperature, runoff, and water demand under A2 and B2 scenarios. Results indicate a notable temperature increase and reduced rainfall in the 2070-2099 compared to 2040-2069. Temperature and precipitation variations in the B2 scenario show a more significant temperature rise and precipitation reduction in the 2070-2099 than in 2040-2069.Runoff simulation results demonstrate that the Artificial Neural Network (ANN) outperforms IHACRES, leading to its adoption for future flow predictions. The simulated monthly long-term average runoff using the ANN model in the A2 scenario decreased by 15.7% and 20.9% in the 2040-2069 and 2070-2099 periods, respectively. In the B2 scenario, the reductions were 17.2% and 26.5% compared to the baseline period.Findings from the CROPWAT model indicate a rise in long-term average monthly water demand in future periods. Under scenario A2, demand increased by 100% and 170% in 2040-2069 and 2070-2099, respectively. Under scenario B2, demand increased by 94% and 121% compared to the baseline period. Water demand growth is more pronounced in the A2 scenario and in the 2070-2099 period compared to 2040-2069.
The time series analysis of demand and release optimization using GWA and PSO shows the system's attempt to maximize supply and reduce vulnerability, thereby minimizing failure intensity. The results indicate that GWA performs better than PSO in achieving this objective.

This study examines the impact of climate change on water resources and consumption in the Zayandehrud Basin, focusing on future periods. Additionally, optimal reservoir operation strategies for the Zayandehrud Dam have been assessed. The findings indicate:Runoff under the A2 scenario will decrease by 15.7% in 2069–2040 and 20.9% in 2099–2070, while the B2 scenario will see reductions of 17.2% and 26.5%, respectively. Water demand will rise significantly, with an increase of 100% in 2069–2040 and 170% in 2099–2070 under A2, whereas B2 will exhibit a 94% increase in 2069–2040 and 121% in 2099–2070. Under baseline conditions, system reliability was 99.81% for GWA and 99.72% for PSO, while vulnerability was 0.53% for GWA and 0.71% for PSO. In the A2 scenario (2069–2040), reliability values for GWA and PSO were 89% and 88%, with corresponding vulnerability values of 9.8% and 10.1%. In A2 (2099–2070), reliability values for GWA and PSO declined to 80% and 79%, while vulnerability rose to 12.8% and 13.3%, respectively. In B2 (2069–2040), reliability values for GWA and PSO were 89% and 88%, while vulnerability was 10.4% and 10.7%, respectively. In B2 (2099–2070), reliability values dropped to 76% for GWA and 73% for PSO, with vulnerability increasing to 18.9% and 19.4%, respectively. Comparing both models reveals that GWA outperforms PSO by achieving higher reliability and lower vulnerability in reservoir operation.

Achieving optimal and safe energy management and planning, taking into account the reduction of electricity production, transmission and distribution costs, as well as the reduction of environmental pollutants, has become increasingly important in many power companies in developing countries. With the optimal use of renewable energy sources and the use of a secure platform, including blockchain technology, the aforementioned goals can be achieved. To solve optimization problem, after modeling the hybrid AC-DC microgrid and considering the high complexity of the proposed formulation, the grey wolf optimization algorithm is proposed to solve the problem. In order to check the efficiency of the system under cyber-attacks, the system is subjected to false data injection attacks in different parts of the system, and then the operation is done under normal conditions and cyber-attacks. In this paper, MATLAB software package is used to solve the optimization problem and modeling the cyber-attacks. The operation results have been examined in different scenarios and the negative effects of such attacks are shown by comparing with the normal state. Then, to enhance the security of the system and prevent attacks, blockchain technology is presented to increase the security of the data exchanged in the system.

Economic dispoatch in the operation of modern power systems is of great importance. The economic load dispatch, taking into account all constraints, needs to be allocated among different multi-area generators. Economically distributing the load in multi-area economic dispatch (MAED) can simultaneously satisfy multiple constraints. In this regard, the use of algorithms can be helpful. This study aimed to determine the decentralized economic power dispatch in the transmission system using Grey Wolf Optimization (GWO) and Wild Horse Optimization (WHO) algorithms. In this study, a combination of GWO and WHO algorithms, aided by a fuzzy logic system, was employed in MATLAB software to obtain the optimal power generation schedule for a 24-hour period in the presence of HVDC and HVAC transmission lines. The results of this study on the IEEE 118 BUS network demonstrated that the GWO and WHO algorithms, considering point load, achieved satisfactory results in reducing losses and generation costs. Therefore, the use of these algorithms is recommended for improving decentralized economic power dispatch in the transmission system.

One of the indicators for evaluating the performance of a tunnel drilling machine is predicting the penetration rate of this machine. There are various methods and relationships for predicting the penetration rate, each of which has its own characteristics and are presented based on the parameters related to the rock mass and the characteristics of the machine. In this study, genetic, artificial immune system, dolphin echolocation and grey wolf algorithms were used to predict the penetration rate of TBM. In this regard, the database consists of 153 data (122 data for train and 31 data for test) including parameters of intact rock such as strength and brittleness and rock mass characteristics such as distance between planes of weakness and orientation of discontinuities along with TBM machine performance in Queens tunnel has been collected. Mean square error (MSE) and square correlation coefficient (R2) have been used to estimate the error rate between the developed methods. Considering the key parameters of rock mass and intact rock and TBM, relationships to predict the penetration rate are presented and based on statistical analysis, the best relationship is selected. The results are compared with the real data and the results of other models show that the values penetration rate predicted by the genetic algorithm with MSETrain=0.012, MSETest=0.02, R2Train=0.9319 and R2Test=0.8473,has acceptable accuracy compared to other methods.

In this paper, an effective joint damage identification method has been proposed. Beam to columns connections modeled as a rotational mass less spring. Joint damage has been presented as a reduction in the connection rigidity or rotational stiffness factor. Because of sensitivity of mode shapes and frequencies on structural stiffness, the first mode shape and frequency of damaged frame has been used as a feature to identify damage in steel frame connections. This data is acquired by the modal analysis of damaged structure applying the finite element method (FEM). The numerical studies are carried out within the MATLAB (2016) environment, which is used for the solution of finite element problems. To identify joint damage, an optimization problem formulated in which the objective functions formulated based on Modal Assurance Criterion (MAC) and MAC flexibility. To solve the optimization problem, an effective meta-heuristic called Grey Wolf Optimizer is employed to detect damage in beam to columns connections. To evaluate the performance of presented method, three examples consists of a seven, twelve and fifteen story steel frames are chosen with two different scenarios of damage in beam to columns connections for each of them for this purpose. Results reveal that the proposed approach is effective to detect and estimate damage in steel frame connections.