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Differential evolution is a high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a differential evolution optimizer is implemented and compared to a particle swarm optimization for control of a first-order process with a time delay, using fuzzy PID, and PID controller. The results show that the optimization scenarios are better suited to differential evolution versus the other. The differential evolution optimizer shares the ability of the genetic algorithm to handle arbitrary nonlinear cost functions, but with a much simpler implementation and a better performance it clearly demonstrates good possibilities for widespread use in controller optimization.