Some civil engineering-based infrastructures are planned for the structural health monitoring (SHM) system based on their importance. Identifiction and detecting damage automatically at the right time are one of the major objectives this system faces. One of the methods to meet this objective is model updating whit use of optimization algorithms in structures.This paper is aimed to evaluate the location and severity of the damage combining two being-updated parameters of the flexibility matrix and the static strain energy of the structure using augmented grey wolf optimization (AGWO) and only with extracting the data of damaged structure, by applying 5 percent noise. The error between simulated and estimated results in average of ten runs and each damage scenario was less than 3 percent which proves the proper performance of this method in detection of the all damages of the 37-member three-dimensional frame and the 33-member two-dimensional truss. Moreover, they indicate that AGWO can provide a reliable tool to accurately identify the damage in compare with the particle swarm optimizer (PSO) and grey wolf optimizer (GWO).

Multilevel image thresholding is essential for segmenting images. Expectation Maximization (EM) is effective for finding thresholds; but, it is sensitive to starting points. The grey wolf Optimizer (GWO) is fast at finding thresholds but can get stuck in local optima. This paper presents a new algorithm, EM+GWO, combining both methods to improve segmentation. EM estimates Gaussian Mixture Model (GMM) coefficients, while GWO finds better solutions when EM is stuck. GWO adjusts GMM parameters using Root Mean Square Error (RMSE) for the best fit. The algorithm was tested on nine standard images, evaluating global fitness, PSNR, SSIM, FSIM, and computational time. The results show that EM+GWO significantly enhances image segmentation effectiveness. Statistical tools indicate that RCG achieves the best RMSE and PSNR in 7 out of 9 test images, and it holds the highest rank in both SSIM and FSIM. The average execution time of each algorithm was calculated, showing that EM+GWO has an acceptable running time compared to EM and GWO. This balance between computational efficiency and improved segmentation performance makes the proposed EM+GWO algorithm a robust and effective solution for image segmentation tasks. Overall, the combination of EM and GWO methods provides a more reliable and accurate approach to optimizing image segmentation, avoiding local optima, and enhancing overall performance.

Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i. e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i. e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance.

This paper presents the optimization of a system of square cascades for separating the middle components of xenon. It also presents the optimal use of square cascades in this system. As an example, the separation of 130 Xe, an element whose middle isotope is much more complex than any of the other isotopes of xenon, is provided. The grey wolf Algorithm is applied for optimization. and the parameters of cascade feed flow rate, cut off the cascade, feed location, feed flow of gas centrifuges (GC), and the cut of the first stage are optimized in such a way that the maximum recovery of the target isotope and the maximum cascade capacity are achieved. Based on the optimization results, the more steps the cascade has, the fewer separation steps are needed for the nine selected cascades with 180 GCs. As a result, both the recovery factor and the amount of product increase.

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.