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In nanometer complementary metal-oxide-semiconductor technologies, worst-case design methods and response-surface-based yield optimization methods face challenges in accuracy. Monte-Carlo (MC) simulation is general and accurate for yield estimation, but its efficiency is not high enough to make MC-based analog yield optimization, which requires many yield estimations, practical. In this paper, techniques inspired by computational intelligence are used to speed up yield optimization without sacrificing accuracy. A new sampling-based yield optimization approach, which determines the device sizes to optimize yield, is presented, called the ordinal optimization (OO)-based random-scale differential evolution (ORDE) algorithm. By proposing a two-stage estimation flow and introducing the OO technique in the first stage, sufficient samples are allocated to promising solutions, and repeated MC simulations of non-critical solutions are avoided. By the proposed evolutionary algorithm that uses differential evolution for global search and a random-scale mutation operator for fine tunings, the convergence speed of the yield optimization can be enhanced significantly. With the same accuracy, the resulting ORDE algorithm can achieve approximately a tenfold improvement in computational effort compared to an improved MC-based yield optimization algorithm integrating the infeasible sampling and Latin-hypercube sampling techniques. Furthermore, ORDE is extended from plain yield optimization to process-variation-aware single-objective circuit sizing.