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.

[1]  Halim Ceylan,et al.  Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey , 2008 .

[2]  Arun Kumar Sangaiah,et al.  Energy Consumption in Point-Coverage Wireless Sensor Networks via Bat Algorithm , 2019, IEEE Access.

[3]  M. Toksari Ant colony optimization approach to estimate energy demand of Turkey , 2007 .

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Amin GhasemiNejad,et al.  Forecasting Iran’s Energy Demand Using Cuckoo Optimization Algorithm , 2019, Mathematical Problems in Engineering.

[6]  Arun Kumar Sangaiah,et al.  A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm , 2020, Soft Comput..

[7]  Chee Peng Lim,et al.  An artificial bee colony algorithm with a modified choice function for the Traveling Salesman Problem , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[8]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[9]  Naveen K. Chilamkurti,et al.  IoT Resource Allocation and Optimization Based on Heuristic Algorithm , 2020, Sensors.

[10]  Turan Paksoy,et al.  Swarm intelligence approaches to estimate electricity energy demand in Turkey , 2012, Knowl. Based Syst..

[11]  A. Ghanbarzadeh,et al.  Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand est , 2010 .

[12]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[13]  Debahuti Mishra,et al.  A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data , 2012 .

[14]  Mojtaba Bahmani,et al.  A novel approach to forecast global CO2 emission using Bat and Cuckoo optimization algorithms , 2020, MethodsX.

[15]  Arun Kumar Sangaiah,et al.  Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem , 2020, Soft Comput..

[16]  Arun Kumar Sangaiah,et al.  A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines , 2019, Symmetry.

[17]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[18]  M. Bahmani‐Oskooee,et al.  Black Market Exchange Rate, Currency Substitution and the Demand for Money in LDCs , 2006 .

[19]  Amin GhasemiNejad,et al.  The Phillips curve in Iran: econometric versus artificial neural networks , 2019, Heliyon.

[20]  M. Ghalambaz,et al.  Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm) , 2011 .

[21]  Jinying Xu,et al.  A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment , 2018, ICA3PP.