Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimiza-
tion problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distribu-
tions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17benchmark functions, and the experimental results demon- strated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness land-
scape, then speeds up convergence to the global optimum, and avoids falling into local optima.

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