差分進化演算法在1995年被提出，差分進化演算法具有強大的求解的能力，其優異求解能力皆適用於複雜的且不同領域的優化問題。差分進化演算法為演化式計算，存在與許多演化式計算相同的問題，易陷入區域最佳解。有鑑於此，本研究提出以動態的突變權重方式改良差分進化演算法，提高演算法跳脫區域最佳解的能力。

Differential Evolution (DE) algorithm, first published in 1995, has proved to be a powerful tool for complicated optimization problems. Its outstanding performance makes it applicable to different application fields. However, since differential evolution belongs to the Evolutionary Algorithm, problems like trapping in local optimum may also occur in this algorithm. In order to improve the performance of differential evolution, a novel algorithm will generate a dynamical “mutation factor”. By this
approach, we can increase the performance of DE algorithm and avoid dropping into local optimums.