long-term prediction of the crude oil price using a new particle swarm optimization algorithm

Oil is one of the most precious source of energy for the world and has an important role in the globaleconomy. therefore, the long-term prediction of the crude oil price is an important issue in economy andindustry especially in recent years. the purpose of this paper is introducing a new particle swarm optimization(pso) algorithm to forecast the oil prices. indeed, the pso is a population-based optimizationmethod inspired by the flocking behavior of birds. its original version suffers from tripping in local minima.here, the pso is enhanced utilizing a convergence operator, an adaptive inertia weight and linearacceleration coefficients. the numerical results of mathematical test functions, obtained by the proposedalgorithm and other variants of the pso elucidate that this new approach operates competently in termsof the convergence speed, global optimality and solution accuracy. furthermore, the effective variableson the long-term crude oil price are regarded and utilized as input data to the algorithm. the objectivefunction of the optimization process considered in this research study is the summation of the square ofthe difference between the actual and the predicted oil prices. finally, the long-term crude oil prices areaccurately forecasted by the proposed strategy which proves its reliability and competence.