gray wolf algorithm

Air travel delays are inevitable and play a very important role in the profits and losses of airlines. By predicting or estimating delays, the benefits of agencies and customer satisfaction can be increased to some extent. Flight delays change flight schedules as well as increase organization costs. In this regard, a lot of work has been done, including scheduling and reallocation of gates, most of which tried to allocate the gate without considering flight delays and only due to limitations and the use of available resources. Since the variables affecting the gate allocation are uncertain and there is no known algorithm to find the optimal solution for it in a limited time, this issue is considered NP-hard problem and is subject to different conditions and policies of airports and agencies. In this paper, in case of delay, a hybrid system based on the Gray Wolf algorithm and fuzzy allocation logic is designed, which up to date the gate allocation list according to the uncertainties and using the data related to the delay prediction. The proposed model was implemented and tested using Boston data in a MATLAB environment. The results of the model evaluation showed that the proposed model has improved the gate allocation list so that some gates remain empty, which means that the number of flights can be increased and the organization's profit can be increased without changing the physical infrastructure of the airport.

Compacted soils, which are commonly used in geotechnical engineering projects, such as earth dams, highways, embankments, and airport runways, are mostly unsaturated. To achieve a safe design in all these projects, the stress state variable in soil plays a significant role. Any proposed model for the stress state variable should express its independence from the soil properties. In saturated soils, the e effective stress is taken into account as the stress state variable. Some researchers have attempted to find the stress state variable for unsaturated soils the same as that for saturated soils with only one variable; however, they have noticed that the soil properties have been involved in the proposed models (Bishop, 1959; Escario and Saez, 1986; Khalili and Khabbaz, 1998; Lu and Likos, 2004; Rahnema et al., 2019). The purpose of this paper is to apply new intelligent methods to accurately estimate the effective stress parameter, using two gray wolf optimization (GWO) and sine-cosine (SCA) optimization algorithms.

With the increasing expansion of urban environments, the creation and development of intra-city transportation systems in order to reduce traffic, pollution and reduce the costs of intra-city traffic is essential. Considering that an important part of the construction cost of the metro is related to the excavation and maintenance of tunnels. Therefore, one of the most important decisions in the construction of subway tunnels is the excavation method in alluvial and fall environments. Tunnel excavation by TBM-EPB machine is a fast, powerful and maintenance method compared to other excavation methods in soft soils and fall areas. One of the most important factors in preventing the face pressure from falling during excavation in soft and alluvial fields is estimating the optimal face pressure of the excavation machine in each excavation stage (different kilometers). Because the high or low face pressure of the excavation machine leads to increased costs, loss of life, high hardness and also leads to delays in the completion of the project. In this paper, due to the uncertainty in geotechnical parameters and the sensitivity of urban tunnels, the issue has been studied from a probabilistic perspective. For this purpose, first for 50 different numerical modeling modes of Tabriz Metro Line 2 using PLAXIS3D2020 software and then Monte Carlo simulation method has been used to generate random numbers and assign appropriate probabilistic distributions. Then, using the Gray Wolf meta-heuristic algorithm (GWO), the face pressure of the TBM-EPB machine was estimated using the prediction relation. Finally, in order to evaluate and validate the relationship, the statistical indicators of square correlation coefficient (R2), variance inclusion (VAF), mean absolute error percentage (MAPE), root mean square error (RMSE) and mean square error (MSE) were used. Is. According to the model validation, the relationship created by the gray wolf algorithm is very close to the reality of the problem and it can be used to continue the route in other similar areas.

Acquiring optimal dimensions of concrete dams by reducing the cross section will reduce the volume of concrete used and consequently reduce construction costs. Due to multiplicity of constraints which leads to complexity of the decision space, meta-heuristic optimization algorithms have been increasingly used in optimal design of gravity dams. In this study, performance of Grey Wolf Optimizer was assessed in optimal design of Koyna concrete gravity dam and the results were compared to Particle Swarm Optimization. Results indicated that Grey Wolf Optimization was able to reach to a 5.2 percent lower mean optimal cost over 10 runs result with considerably a 61.5 percent smaller number of function evaluations compared to Particle Swarm Optimization and considering stability of the algorithm results obtained from Grey Wolf Optimization had smaller standard deviation than Particle Swarm Optimization. However, in 10 consecutive runs, the minimum optimal value reached using Particle Swarm Optimization was 1.6% smaller than the smallest optimal value obtained from Grey Wolf Optimization, considering overall performance of algorithms, Grey Wolf Optimization presented a promising performance having a faster and stable convergence

Nowadays, controlling vibration of a structure in earthquakes and reducing the structural responses are of great importance. Dampers are a type of systems controlling the absorption of the earthquake energy and dissipating it in order to reduce the structural responses. The main objective of this paper is to find the optimal characteristics of the nonlinear viscose dampers placed at all storeys in a three-storey single bay concrete frame. It is assumed that the structure is subjected to the Elcentro and Gazli earthquakes; and both linear and nonlinear behaviors are considered for the structure during the earthquakes. OpenSees software is used to model the concrete frame and nonlinear viscous dampers. The structural responses are calculated at different time steps, using Newmark's numerical method. A series of constraints which are considered during the optimization process include the limits for parameters of nonlinear viscous damper (Viscous coefficient, axial stiffness and damper exponent) and the maximum absolute value for base shear. The objective is to find optimal values for these parameters in such a way that the maximum absolute value would be minimized for the roof displacement during the earthquake load. The results show that the nonlinear viscous damper is very suitable to control the structure responses when it has linear behavior. In fact, when the structure is subjected to the Elcentro earthquake, the maximum absolute value of the roof displacement and base shear decrease by 37.51% and 43.39%, respectively. The nonlinear behavior of the concrete frame does not improved considerably when the nonlinear viscous dampers are used to control the nonlinear response of the structure in the earthquake.

In recent decades, the optimal use of dam reservoirs among water resource management researchers has been of great interest. So, due to the high performance and capabilities of evolutionary algorithms, in this study, using gray wolf optimizer algorithm (GWO) to predict Urmia Shaharchay dam reservoir and present a short-term forecast program for next years. The gray wolf algorithm imitates the hierarchy of leadership and the mechanism of hunting gray wolves in nature. In this algorithm, four types of gray wolves consists of alpha, beta, delta and omega have been used to simulate the hierarchy of leadership. In this study, considering the annual planning and monthly intervals, GWO algorithm was firstly evaluated for prediction storage of Urmia shaharchay reservoir during 2006-202014 years and the results compared with ICA algorithm. The results showed that GWO algorithm, with a high accuracy of 90%, provides better results in finding optimal response, convergence rate and lower computational cost compared to ICA algorithm. The results of this study indicated that GWO algorithm, an appropriate algorithm to solve the optimal operation of dam reservoir system problem.