Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm

Abstract

Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points  and   are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables  and  determine the performance of the simulated annealing method, we can adjust them according to the optimized case.Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points  and   are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables  and  determine the performance of the simulated annealing method, we can adjust them according to the optimized case.