Using linear, nonlinear, and dynamic planning methods for water resources management has been common since a long time ago, but owing to some deficiencies, today much attention is paid to heuristics methods. Among the optimization algorithms, the mothfire algorithm can be considered. In this paper, the optimization of the flood management plan was carried out using the moth-fire algorithm. In order to consider the flood damage in each month, the estimated damage values are determined according to the floods routing with different return periods in the downstream of the dam using MATLAB software. The sum of the expected damage of flood and lack of need supply in the objective function will be minimized using the moth-fire algorithm. The results of a case study carried out on the Aras dam indicate the efficiency of the proposed optimization model in supplying the needs and reducing the flood damage in the downstream.

The focus is on the longevity and energy efficiency of wireless sensor networks (WSN). WSNs face many obstacles in terms of data transmission. WSNs face difficulties in reducing energy output and shortening life cycles, including node configuration, leader selection, and optimal routing selection. The provisioning of nodes, selection of cluster leaders, and optimal paths have all been recommended using many current methods. However, none of the currently used methods yield sufficient grid energy optimization results. Therefore, this study proposes a modified moth flame optimization algorithm (MFOOA). Nature passed it on to us. The main inspiration for this optimizer is the lateral flight pattern used by moths in nature. At night, the moth maintains a constant angle to the moon. This is a particularly efficient way to drive long distances in a straight line. Nevertheless, artificial light is everywhere around these amazing creatures, encircling them in a fruitless and deadly spiral. Here, this behavior is theoretically modeled for optimization. The suggested program places the sensor nodes using the flame optimization technique. These sensor nodes might be either dynamic or static depending on the network scenario. The cluster head and the optimum route are chosen using this technique. Within the predetermined search space, it also does phase balancing between the exploration and development phases. In terms of residual energy, sensor node lifetime, used energy, end-to-end latency, and a maximum number of cycles, it differs from current classical and swarm intelligence (SI) techniques. According to the results, MFOOA is superior to its counterpart.

FinFETs are the emerging 3D-transistor structures due to strong electrostatic control of active channel by gate from more than one side which was not possible in conventional transistor. FinFET structures with rectangular and trapezoidal shape have been excessively analyzed in literature. The main purpose of this work is to present a FinFET structure with such a compact fin shape that the gate has high controllability over it; and thus reduced short channel effects in comparison to existing structures. Here, FinFET with Broadwell-Y shape, proposed by Intel has been designed and its short channel effects were analysed. Simulations of the designed FinFET have been performed in Technology Computer Aided Design (TCAD) tool. Performance of broadwell-Y shaped FinFET was compared with the existing rectangular and trapezoidal structures for the same input design parameters and it was noticed that Broadwell-Y shaped FinFET outperformed the last two structures in terms of short channel effects. Then the performance of the designed device was optimized using moth flame optimization (MFO) after the network was trained through Artificial Neural Network (ANN). Results obtained from MATLAB were in close agreement with those obtained from TCAD simulations. Output parameters like leakage current (IOFF) of 2.407e-12A, On-Off current ratio (ION/IOFF) of 4.5e06, Subthreshold Swing (SS) of 65.4mV/dec and Drain Induced Barrier Lowering (DIBL) of 37.9mV/V were obtained after optimization. Short channel effects are improved for 20nm gate length as SS is close to ideal value 60mV/dec and DIBL is below 100mV/V which makes this designed structure a good option for applications at nanoscale.

Data clustering is a widely used technique in various domains to group data objects according to their similarity. Clustering molecules is a useful process where you can easily subdivide and manipulate and large datasets to group compounds into smaller clusters with similar properties. To dis-cover new molecules with optimal properties and desired biological activity, can be used by comparing molecules and their similarities. A prominent clustering technique is the k-means algorithm, which assigns data objects to the nearest cluster center. However, this algorithm relies on the ini-tial selection of the cluster centers, which can affect its convergence and quality. To address this issue, metaheuristic algorithms have been proposed as a type of approximate optimization algorithm capable of identifying almost optimal solutions. In this paper, a new meta-heuristic approach is proposed by combining two algorithms of particle swarm optimization (PSO) and moth flame optimization (MFO), following that, it is used to improve data clustering. The  fficiency of the proposed approach is evaluated utilizing benchmark functions F1-F23. Its efficiency is evaluated with PSO and MFO algorithms on different datasets. Our experiential results show that the suggested approach exceeds the PSO and MFO algorithms with respect to speed of convergence and clustering quality.

The performance of photovoltaic (PV) systems is highly dependent on environmental conditions. Due to probable changes in environmental conditions, the real-time control of PV systems is essential for exploiting their maximum possible power. This paper proposes a new method to track the maximum power point of PV systems using the moth-flame optimization algorithm. In this method, the PV DC-DC converter’ s duty cycle is considered as the optimization parameter, and the delivered power of the PV system is maximized in real time. In the proposed approach, some schemes are also employed for detecting condition changes and ignoring small fluctuations of the duty cycle. The results of performance evaluation confirm that the proposed method is very fast, robust, and accurate in different conditions such as standard irradiance and temperature, irradiance and temperature variations, and partial shading conditions. The obtained steady-state efficiency and response time for the introduced method under the standard conditions of the test PV system are 99. 68% and 0. 021 s, respectively. Indeed, in addition to a relatively good efficiency, the faster response of the introduced tracker is also evident in comparison with other methods.

The stock market involves risks and returns that, if forecasted correctly, can lead to profitability, and for this forecasting, appropriate methods are needed. It is affected by various parameters and needs a way to identify these parameters well and have a dynamic nature. The main goal of this article is forecasting Tehran Price Index (TEPIX) by using hybrid Artificial Neural Network (ANN) based on Genetic algorithm (GA), Harmony Search (HS) particle Swarm optimization algorithm (PSO) moth flame optimization (MFO) and Whale optimization algorithms. GA is used as feature selection. So, PSO, HS MFO and WOA are used to determine the number of input and hidden layers. We use the daily values of the stock price index of the Tehran Stock Exchange from 2013 to 2018 in order to forecasting price and test it. The accuracy of ANN, hybrid Artificial Neural Network with HS, PSO MFO and WOA is evaluated based on different loss functions such as MSE, MAE and etc. the results show that the predictability of Meta-heuristic algorithms in testing period is higher than normal ANN. Also, the predictability of hybrid WOA is higher than hybrid PSO and HS algorithms and MFO.

One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes: controlling liquidity, inflation, and prices, Economic growth and development, lending, etc. So, it is important to set the interest rate correctly. If you can predict the interest rate correctly, you can earn and gain profit by investing in various sectors. Moreover, the interest rate can impact other sectors through parallel markets such as the stock market, automobile, housing, etc. Interest rates are related to parallel markets. Thus, if you can forecast the interest rate, you can predict the parallel markets too. The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as moth flame optimization algorithm (MFO), Chimp optimization algorithm (CHOA), Time-varying Correlation Particle Swarm optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. We used GA and a new algorithm called Grey Wolf optimization, Particle Swarm optimization (GWO-PSO) algorithm as a feature selection and choosing the best variables. We have used eight loss functions such as MSE, RMSE, MAE, etc. too. Finally, we have compared different algorithms due to their estimation errors. The main contribution of this paper is that, first, this is for the first time which these novel metaheuristic algorithms have been used for the prediction of interest rate. Second, it has tried to use different graphs and tables for better understanding and totally a comprehensive research paper. The results show that Whale optimization algorithm (WOA) performed better than other methods along with less error.