A rotationally invariant stochastic opposition-based learning using a beta distribution in differential evolution

Abstract

A computational technique called opposition-based learning (OBL) has gained a lot of attention in the field of soft computing. Among the different forms of OBL, the iBetaCOBL variant, which is a stochastic OBL, has shown the best results in improving the performance of differential evolution (DE). However, this approach may not be effective in handling complex problems as it is susceptible to rotations in the coordinate system. To address this issue, we present a new and improved version of iBetaCOBL called iBetaCOBL-eig. This new technique uses an eigenvector-based multiple exponential crossover operator in the partial dimensional change method, making it rotationally invariant. We conducted experiments to evaluate the performance enhancements of DE using iBetaCOBL-eig on 29 challenging benchmark problems. Our results showed that the new algorithm is able to outperform not only ten strong OBL variants but also its previous version iBetaCOBL.