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This paper presents a new distributed Differential Evolution (dDE) algorithm and evaluates it according to the standard procedure set in the special session of continuous optimization of CEC'05. We statistically validate our results in continuous optimization versus several other efficient techniques. Our distributed Differential Evolution is simple and accurate, at the same time amenable, to be applied to a wide variety of problems, especially for noisy and multimodal functions.

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