Improving Equlibrium Optimization Algorithm Performance with Optimization to Optimization Approach

Abstract

In this study, the performance of the Equilibrium Optimization (EO) algorithm has been improved with the optimization to optimization (OO) approach. While the EO algorithm has been used as the basic algorithm in the OO approach, the Stochastics Multi-parameters Divergence Optimization (SMDO) algorithm is used as the auxiliary algorithm. The proposed structure has been tested on benchmark functions. In this study, three basic parameters of the Equilibrium Optimization algorithm have been optimized separately for the benchmark functions used in the study by using the SMDO method. Although these three parameters were kept constant in the original algorithm, they have been tried to be optimized with an upper algorithm without changing the basic philosophy of EO in order to increase the current optimization performance by ensuring that they take more appropriate values in this study. Thus a strong algorithm that can produce more efficient and better results for the existing problem has been tried to be created with the optimization to optimization approach. As a result, the obtained algorithm has been tested with the most well-known benchmark functions in the literature and the results obtained have been presented in comparison with the results of the original Equilibrium Optimization algorithm.