中文说明：DE 算法主要用于求解连续变量的全局优化问题，其主要工作步骤与其他进化算法基本一致，主要包括变异(Mutation)、交叉(Crossover)、选择(Selection)三种操作。算法的基本思想是从某一随机产生的初始群体开始，利用从种群中随机选取的两个个体的差向量作为第三个个体的随机变化源，将差向量加权后按照一定的规则与第三个个体求和而产生变异个体，该操作称为变异。然后，变异个体与某个预先决定的目标个体进行参数混合，生成试验个体，这一过程称之为交叉。如果试验个体的适应度值优于目标个体的适应度值，则在下一代中试验个体取代目标个体，否则目标个体仍保存下来，该操作称为选择。在每一代的进化过程中，每一个体矢量作为目标个体一次，算法通过不断地迭代计算，保留优良个体，淘汰劣质个体，引导搜索过程向全局最优解 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报
English Description：
DE algorithm is mainly used to solve global optimization problems with continuous variables, and its main steps are basically consistent with other evolutionary algorithms, including three operations: mutation, crossover and selection. The basic idea of the algorithm is to start from a randomly generated initial population, use the difference vector of two individuals randomly selected from the population as the random change source of the third individual, weigh the difference vector and sum with the third individual according to certain rules to generate a mutation individual, which is called mutation. Then, the mutation individuals and a predetermined target individuals are mixed to generate test individuals. This process is called crossover. If the fitness value of the test individual is better than that of the target individual, the test individual will replace the target individual in the next generation, otherwise the target individual will still be preserved, which is called selection. In the evolution process of each generation, each individual vector is regarded as the target individual once. Through continuous iterative calculation, the algorithm keeps the good individuals, eliminates the poor individuals, and guides the search process to the global optimal solution