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This paper introduces a new sampling technique called Opposite-Center Learning (OCL) intended for convergence speed-up of meta-heuristic optimization algorithms. It comprises an extension of Opposition-Based Learning (OBL), a simple scheme that manages to boost numerous optimization methods by considering the opposite points of candidate solutions. In contrast to OBL, OCL has a theoretical foundation-the opposite center point is defined as the optimal choice in pair-wise sampling of the search space given a random starting point. A concise analytical background is provided. Computationally the opposite center point is approximated by a lightweight Monte Carlo scheme for arbitrary dimension. Empirical results up to dimension 20 confirm that OCL outperforms OBL and random sampling: the points generated by OCL have shorter expected distances to a uniformly distributed global optimum. To further test its practical performance, OCL is applied to differential evolution (DE). This novel scheme for continuous optimization named Opposite-Center DE (OCDE) employs OCL for population initialization and generation jumping. Numerical experiments on a set of benchmark functions for dimensions 10 and 30 reveal that OCDE on average improves the convergence rates by 38% and 27% compared to the original DE and the Opposition-based DE (ODE), respectively, while remaining fully robust. Most promising are the observations that the accelerations shown by OCDE and OCL increase with problem dimensionality

How to Speed up Optimization? Opposite-center Learning and Its Application to Differential Evolution