| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 97 === Differential Evolution (DE) algorithm, first published in 1995, has proven to be a powerful tool for complicated optimization problems. Its outstanding performance and accuracy makes it applicable to different research fields. However, since differential evolution belongs to the Evolutionary Algorithm, problems like dropping in local optimum may also occur in this algorithm. In order to improve the performance of differential evolution, we propose a novel algorithm, which will generate a dynamical function for changing the differential evolution parameter "mutation factor" replace traditional differential evolution algorithm use constant mutation factor. The present study is to increase the performance of this novel algorithm and to avoid dropping into local optimum.
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