Parameter setting of meta-heuristic algorithms: a new hybrid method based on DEA and RSM

The parameter setting of meta-heuristic algorithms is one of the most effective issues in the performance of meta-heuristic algorithms and is usually done experimentally which is very time-consuming. In this research, a new hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis method and response surface methodology, called DSM. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the hybrid DSM method has been used to set the parameters of the cuckoo optimization algorithm to optimize the standard and experimental functions of Ackley and Rastrigin. In addition to standard functions, in order to evaluate the performance of the proposed method in real problems, the parameter of reverse logistics problem for COVID-19 waste management has been adjusted using the DSM method, and the results show better performance of the DSM method in terms of solution time, number of iterations, efficiency, and accuracy of the objective function compared to other.