Harris Hawks Optimization (HHO) algorithm, which is a new metaheuristic algorithm that has shown promising results in comparison to other optimization methods. The surprise pounce is a cooperative behavior and chasing style exhibited by Harris' Hawks in nature. To address the limitations of HHO, specifically its susceptibility to local optima and lack of population diversity, a modified version called Modified Harris Hawks Optimization (MHHO) is proposed for solving global optimization problems. A mutation-selection approach is utilized in the proposed Modified Harris Hawks Optimization (MHHO) algorithm. Through systematic experiments conducted on 23 benchmark functions, the results have demonstrated that the MHHO algorithm offers a more reliable solution compared to other established algorithms. The MHHO algorithm exhibits superior performance to the basic HHO, as evidenced by its superior average values and standard deviations. Additionally, it achieves the smallest average values among other algorithms while also improving the convergence speed. The experiments demonstrate competitive results compared to other meta-heuristic algorithms, which provide evidence that MHHO outperforms others in terms of optimization performance.

Today, due to existing economic issues, optimization and maximum use of materials are highly regarded. Due to the wide range of parameters, the use of mathematical methods is not logical. For this reason, meta-heuristic methods have expanded. In the field of structures, weight optimization using various methods is of great interest. Due to the importance of truss, in this paper, the optimization of truss has been done using a hybrid algorithm of Harris Hawks and genetics. The Harris Hawks algorithm is one of the newest algorithms in the field of optimization, which is derived from the natural behavior of animals. In the Harris Hawks algorithm, the mutation process, which belongs to the genetic algorithm, is used. The optimization is constrained; therefore the constraints of stress and displacement have been selected. Four trusses, planer 10-bar truss, spatial 25‑ bar truss, spatial 72‑ bar space truss and planner 200 bar truss have been selected for optimization. The implementation of Harris Hawks algorithm has been done in MATLAB software. The results obtained from Harris Hawks-genetic algorithm are compared with other available sources. The study shows the acceptable performance of this hybrid algorithm for truss. The Harris Hawks-genetic hybrid algorithm has faster convergence speed.

This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.

The present study focuses on the Harris Hawks optimizer. Harris Hawks optimization (HHO) is introduced based on population and nature patterns. The  HHO algorithm imitates Harris Hawks attacking behavior and includes two phases called exploration and exploitation, which can be modeled with three strategies, 1) discovering the prey, 2) surprising attack, and 3) prey attack. The main purpose of using this type of algorithm is to optimally solve the short-term hydro-thermal self-scheduling (STHTSS) problem with wind power(WP), photovoltaic (PV), small  hydro (SH) and pumped hydro storage (PHS) powr plants while considering uncertainties such as energy prices, ancillary services prices, etc, in the energy market. It will be shown how energy generation companies can use this algorithm and other algorithms and innovative methods that will be introduced in the future to achieve profit maximum with careful scheduling. It is worth mentioning that in this study, the effect of the presence and absence of two important factors, namely valve load cost (VLC) effect  and prohibited  operating  zones (POZs) (with linear modeling) that can affect the profit of units (power plants) has been pointed out. Finally, as shown in this study, several tests perfomed on the IEEE118-bus system validate the precision and credibility of the Harris Hawks optimization algorithm.

The aim of this study is to optimize water resource management in the Jarrahi River Basin with an environmental sustainability approach for the Shadgan International Wetland. The Jarrahi River is one of the rivers in the Persian Gulf and Oman Sea drainage basin, with most of its course located in Khuzestan province. The river's catchment area lies on the southwestern slopes of the Middle Zagros Mountains and is located between 48°45' and 51°10' East longitude and 30°30' to 31°40' North latitude. Its area is 24,300 square kilometers. A water resource planning model for the entire Marun and Jarrahi river system was developed using an optimization approach. The entire Marun-Jarrahi watershed was simulated in a monthly time step over a 60-year period using the WEAP simulator, and five scenarios were defined. The results were then integrated and analyzed using the powerful Shahin Harin meta-exploration algorithm. Based on the results, the release pattern for water utilization within the standard benchmark four and ten tank system using Harris Hawks Optimization (HHO), FPA, and SOS algorithms, the exploitation policies derived from the HHO algorithm with a more optimal release pattern yielded the highest benefit. Additionally, the model with the least water shortage was identified using this approach. These results demonstrate the superior efficiency of the HHO algorithm compared to the other meta-exploration algorithms employed. As a further innovation, study proposes and develops a novel hybrid model combining HHO and Cat Swarm Optimization (CSO) algorithms, referred to as the HHO-CSO algorithm.

This work described and compared the hematological findings of 25 clinically healthy Harris’ s Hawks (Parabuteo unicinctus) in captivity at two different tropical locations: 16 samples from Aguascalientes, which altitude is 1878 mean sea level, and nine samples from Amecameca which altitude is 2650 mean sea level. Blood samples were collected from the brachial vein of each raptor under physical restraint. Significant differences between the two locations were found in some parameters including total, erythrocytes, heterophils, basophils, lymphocytes, and heterophil/lymphocyte ratio. When the results were compared to the reference values, the population of Amecameca showed decreased values of hematocrit (32. 21 ± 13. 72%), hemoglobin (107. 40 ± 45. 60 g L-1) and erythrocytes (1. 98 ± 0. 63 ×1012 per μ L). This work contributed to the knowledge of variations in blood parameters of clinically healthy captive Harris`s Hawks at different tropical locations and sex. The information will enable clinicians to provide appropriate veterinary diagnostics and care to ensure the health and welfare of raptors kept in captivity.

Objective: The objective of this study is to develop a machine learning model for simulating the nitrate concentration. Simulating and predicting nitrate concentration has always been one of the most important issues in the field of water resources management. Method: In this research, after collecting the data, the nitrate concentration data are first clustered using the JNB, then, an SVR model is used for each cluster. The SFFS algorithm is used to select the input variables for the model simultaneously with the training process of this model, then, based on the results of these three models, the average value of the error indices for the training stage (RMSE = 0.2387, MAE = 0.2236, R^ 2=0.9874) and test (RMSE = 0.2474, MAE = 0.2350, R^2=0.9841) are calculated. In this case, the trial and error procedure is used for this work. In the next step, the HHO algorithm is used to determine the optimal value of the parameters of the kernel functions. In this case, the values of R2, MAE and RMSE for the training phase are 0.9961, 0.1169, and 0.1502, respectively, and their values for the test phase are 0.9845, 0.1308, and 0.9978, respectively. Results: Based on the results of this study, firstly, the use of HHO to predict nitrate concentration can significantly increase the accuracy of the SVR model, secondly, the use of different machine learning models together can play an effective role in increasing the accuracy of regression models such as SVR. The results of this study show that the use of data clustering before developing machine learning models can improve the accuracy of nitrate concentration prediction. The HHO-SVR hybrid model has performed better in different clusters with proper selection of kernel function and has provided optimal results. Also, this study emphasizes that the different statistical characteristics of each cluster have a significant effect on the performance of the models. Therefore, to more accurately predict nitrate concentration in groundwater, it is recommended to first cluster the data and then develop a specific model for each cluster. Conclusions: The results of this study show that the use of data clustering before developing machine learning models can improve the accuracy of nitrate concentration prediction. The HHO-SVR hybrid model has performed better in different clusters with proper selection of kernel function and has provided optimal results. Also, this study emphasizes that the different statistical characteristics of each cluster have a significant effect on the performance of the models. Therefore, to more accurately predict nitrate concentration in groundwater, it is recommended to first cluster the data and then develop a specific model for each cluster.

Step spillways are an example of these massive hydraulic structures that, in addition to passing the excess water of the dams, also cause the consumption of flow energy downstream of the dams. Considering the complex hydraulics of the flow on these spillways and the presence of nonlinear limitations, their optimal design is a very difficult problem. In this study, a new framework based on Metaheuristic algorithms, including Harris's hawk Optimization (HHO), gray wolf Optimizer (GWO), invasive weeds Optimization (IWO) and water cycle Algorithm (WCA), considering the minimization of the amount of concrete used in spillway and the maximization of energy dissipation in Spillway toe were developed as objective functions to design these spillways. Algorithms' performance was first checked and validated on basic functions. Then, to achieve the objectives of the study, the spillway of the Siah Bisheh dam was selected as the study dam and the efficiency of the developed models based on the four mentioned algorithms was evaluated on it. The results showed that, in addition to improving the current spillway design in terms of construction costs and dissipation energy, the HHO-based model has good accuracy and convergence compared to other Metaheuristic algorithms. As the comparison of the design obtained from HHO with the current spillway design showed, in addition to a 35% reduction in the volume of concrete consumed, the amount of energy dissipation increased by 15%, which indicates the success of the design model developed in a multi-objective manner using HHO.

Path planning of mobile robots is one of the important issues in the field of robotics. Also, optimizing the path length and crossing the local minima traps are the basic and up-to-date challenges in this field. One of the important methods in path planning of these robots is the artificial potential field method. Because it is based on simple mathematical calculations. One of the most important disadvantages of this method is getting trapped in local minima situations. One approach for solving the problem of local minima is to use optimization methods to find suitable coefficients (attractive and repulsive) and step length that can solve local minima and optimize the path length. The Harris Hawks algorithm is a powerful and new meta-heuristic algorithm in the field of optimization that is based on population and inspired by the life of Harris Hawks in nature. This algorithm has been able to prove its superiority over similar optimization methods in finding the optimal solution, faster convergence, lower computational time and not trapping in local minima. This method has not been used in the path planning of mobile robots. In order to eliminate the disadvantages of the artificial potential field method and to optimize the path length, the Harris Hawks algorithm has been used in this paper. The simulation results showed that the combination of the artificial potential field method and the Harris Hawks algorithm can solve the local minima problem and optimize the path length, increase the path efficiency and reduce the convergence time.