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.

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.

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 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.

optimal and economic operation is one of the main topics in power systems. In this paper, a stochastic single objective framework for GenCoʼ s optimal self-scheduling Unit Commitment under the uncertain condition and in the presence of SH Units is proposed. In order to solve this problem, a new meta-heuristic Optimization technique named antlion optimizer (ALO) has been used. Some of the capabilities of the ALO Algorithm for solving the Optimization problems included: (1) the exploration and utilization, (2) abiding convergence, (3) capable of maintaining population variety, (4) lack of regulation parameters, (5) solving problems with acceptable quality. To approximate the simulation conditions to the actual operating conditions, the uncertainties of the energy price, spinning and non-spinning reserve (operating services) prices, as well as the renewable energy resources uncertainty, are considered in the proposed model. The objective function of the problem is profit maximization and modeled as a mixed-integer programming (MIP) problem. The proposed model is implemented on an IEEE 118-bus test system and is solved in the form of six case studies. Finally, the simulation results substantiate the strength and accuracy of the proposed model.

Today, Optimization is very important and necessary in engineering works and it seems necessary in the field of urban runoff and flood management. In this research, a case study of Darakeh River in Tehran as an important links in flood management and runoff conveying is used to reduce the costs of constructing flood conveying routs and water leakage. In the next step, rainfall values are obtained in return periods of 20, 25, 35, 50, 75 and 100 years by Using hydrological relationships. Then, the output runoff is extracted by modeling the network in numerical software and the output hydrographs of the created model areentered into SWMM software. In order to present the output results of the SWMM model in a better format, the SWMM results are entered into SSA software. The SSA results are linked into the Harris Hawks Optimization (HHO) Algorithm and finally, the construction cost and water leakage functions are optimized. Eventually, the results of HHO are compared with the Particle Swarm Optimization (PSO) Algorithm to check the accuracy of HHO. The value of changes for width and height in bridges and channels are compared in two Algorithms. As a result, the HHO Algorithm reduces the volume of flood and water leakage in different return periods by presenting less cost as compared to the PSO Algorithm.