Although the concept of the opposition has an old history in other fields and sciences, this is the first time that it contributes to enhance an optimizer. This chapter presents a novel scheme to make the differential evolution (DE) algorithm faster. The proposed opposition-based DE (ODE) employs opposition-based optimization (OBO) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of the classical DE. A test suite with 15 benchmark functions is employed for experimental verification. The contribution of the opposite numbers is empirically verified. Additionally, two time varying models for control parameter adjustment of ODE are investigated. Details of the ODE algorithm, the test set, and the comparison strategy are provided.