Optimization of hydropower energy generation by 14 robust evolutionary algorithms.

The use of evolutionary algorithms (EAs) for solving complex engineering problems has been very promising, so the application of EAs for optimal operation of hydropower reservoirs can be of great help. Accordingly, this study investigates the capability of 14 recently-introduced robust EAs in optimization of energy generation from Karun-4 hydropower reservoir. The best algorithm is the one that produces the largest objective function (energy generation) and has the minimum standard deviation (SD), the minimum coefficient of variations (CV), and the shortest time of CPU usage. It was found that the best solution was achieved by the moth swarm algorithm (MSA), with the optimized energy generation of 19,311,535 MW which was 65.088% more than the actual energy generation (11,697,757). The values of objective function, SD and CV for MSA were 0.147, 0.0029 and 0.0192, respectively. The next ranks were devoted to search group algorithm (SGA), water cycle algorithm (WCA), symbiotic organism search algorithm (SOS), and coyote optimization algorithm (COA), respectively, which have increased the energy generation by more than 65%. Some of the utilized EAs, including grasshopper optimization algorithm (GOA), dragonfly algorithm (DA), antlion optimization algorithm (ALO), and whale optimization algorithm (WOA), failed to produce reasonable results. The overall results indicate the promising capability of some EAs for optimal operation of hydropower reservoirs.

Investigating the dimensions of globalization and its impact on poverty in Iran: An improved bat algorithm approach.

Collective intelligence is one of the most powerful optimization techniques based on group behaviors of organisms. Bat algorithm (BA) is an algorithm inspired by the collective action of bats in the wild, presented in 2010 by Yang. Researchers have made several efforts to improve these algorithms. This article investigates the effect of globalization on Iran's poverty by enhancing the performance of BA. As an inescapable reality, globalization has various political, social, and economic dimensions, each with different effects on poverty. In this article, to improve the algorithm's performance, the speed and motion relationships of bats were modified such that to adapt the movement of bats as optimization solutions toward the target. The mutation operator is also used to check all points of the search space to get rid of the optimal local optimization. The study period is the years 1995 to 2017. The results showed that globalization affects Iran's poverty in various dimensions, and the performance of the improved bat collective intelligence algorithm (ISABA) for modeling is better than that of the bat algorithms (BAs).•This article provides a suitable method for researchers to study poverty.•Improved BAs can help researchers solve complex problems.•The results obtained by the collective intelligence algorithms in this paper help researchers in the field of poverty to compare the results of their research with it.

A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran.

Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974-2013) and partly for testing the models (2014-2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. • The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. • The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process.

A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world's carbon dioxide emission.

This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo Optimization Algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases.•The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control.•The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies.

A method for estimating width bands of variables in economics under uncertainty conditions.

This study develops a method to estimate the width bands of variables in economics by fuzzy logic. One of its important features is flexibility in the conditions of economic uncertainty, which can be used to model the uncertainty of external and internal factors on economic variables. In this study, for example, the effect of uncertainty of external factors on the Gini coefficient (income distribution) is investigated. For this purpose, we use the fuzzy logistic smooth transition autoregressive (FLSTAR) model and the Gini coefficient is estimated in three bounds (high, middle and low). The result of this estimation suggest that by appropriate policy making the Gini coefficient can be decreased to the lower bound. Another results of this study is that the authorities should prevent the increase of the Gini coefficient in the middle and upper bands with proper planning for the future. In brief,•This study introduces a novel method for estimating high, low and middle bounds of economic variables under uncertainty conditions.•One practical results of this method is to compare high, medium, and low bands of the variables with their current trends, which is a benchmark for policymaking and evaluating the effectiveness of government's policies.•Programs designed with this method are fast and have low cost.

Poverty modeling in the Islamic Republic of Iran using an ANFIS optimized network with the differential evolution algorithm (ANFIS_DE).

Poverty is a multifaceted phenomenon that its study and analysis from all dimensions requires accurate knowledge. In the past, poverty was measured only by the income approach. That is, only people's incomes were compared to the poverty line. But this approach does not identify other dimensions of poverty. Given the importance of discussing poverty in the economies of developing countries, this article examines and models poverty in the Islamic Republic of Iran. This article presents the internal and external dimensions of poverty in the period 1996-2017. In this paper, to model the poverty in Iran, the ANFIS method optimized with a differential evolution algorithm was used. In this method, a differential evolution algorithm was used to train the ANFIS system instead of the FIS system. To evaluate the strength of the model, mean squared error (MSE), root mean squared error (RMSE), Mean absolute error (MAE), STD_error, Mean_error criteria have been used. The data used in this paper are from the World Development Index (WDI) Database, the World Bank Good Governance Indices, the Heritage Foundation's Economic Freedom Indices, and the United Nations data. This information is related to Iran and in the period (1996-2017). The purpose of this paper is to train the ANFIS network with DE algorithm using time series data and to model the data related to the Iran Multidimensional Poverty Index using the trained network. Multidimensional Poverty Index is a very suitable index for monitoring data. Poverty is in society. With the help of this data, we can assess the trend of poverty and income distribution and welfare in this country. The results of this study showed that training the ANFIS system by differential evolution algorithm, can make a very good improvement in the modeling process and reduce error criteria and improve the accuracy of this method.•This article has been compiled with the aim of modeling poverty in the Islamic Republic of Iran.•The method used in this paper is ANFIS network training using the differential evolution algorithm•The use of evolutionary algorithms to train fuzzy systems and artificial neural networks leads to improved performance.

Estimating the volume of dirty money in Iran.

In this study, the volume of dirty money in Iran was estimated. The data belonged to the period of 1997-2019, and was taken from the Central Bank of Iran (website: https://www.cbi.ir). Fuzzy logic was used to estimate the underground economy. Fuzzy theory can mathematically formulate many variables that are imprecise and ambiguous concepts. This theory is appropriate for reasoning, inference, control, and decision-making under uncertainty. This approach works in conditions of uncertainty. In cases in which the variables are inaccurate, this method is used. Fuzzy set theory is a generalization of the set theory. The underground economy is important in estimating the amount of dirty money and has a positive effect on this amount. The effect of the underground economy was investigated using the vector autoregressive (VAR) and vector error correction (VECM) models.•In this article applied the fuzzy logic, to estimate the underground economy.•The method presented in this article can be useful for Researchers and managers in the monetary trend of economics.•The fuzzy method is the best way to estimate the size of the underground economy because it is a measure of uncertainty.

Estimation of upper and lower bounds of Gini coefficient by fuzzy data.

The data presented in this paper are used to examine the uncertainty in macroeconomic variables and their impact on the Gini coefficient. Annual data for the period 2017 - 1996 are taken from the Bank of Iran website https://www.cbi.ir. We used fuzzy regression with symmetric coefficients to calculate upper and lower bound data of Gini coefficient. Estimated data at this stage can be a very useful guide for policymakers, on the other hand, it is a benchmark for evaluating the effectiveness of government policies. The reason for using fuzzy regression to estimate data on Gini coefficients is the extra flexibility of this model.