Rotation-Based Learning: A Novel Extension of Opposition-Based Learning

Opposition-based learning (OBL) scheme is an effective mechanism to enhance soft computing techniques, but it also has some limitations. To extend the OBL scheme, this paper proposes a novel rotation-based learning (RBL) mechanism, in which a rotation number is achieved by applying a specified rotation angle to the original number along a specific circle in two-dimensional space. By assigning different angles, RBL can search any point in the search space. Therefore, RBL could be more flexible than OBL to find the promising candidate solutions in the complex search spaces. In order to verify its effectiveness, the RBL mechanism is embedded into differential evolution (DE) and the rotation-based differential evolution (RDE) algorithm is introduced. Experimental studies are conducted on a set of widely used benchmark functions. Simulation results demonstrate the effectiveness of RBL mechanism, and the proposed RDE algorithm performs significantly better than, or at least comparable to, several state-of-the-art DE variants.