中文说明：
帝王蝶优化算法，一种新的元启发式优化算法！
在自然界中，北美洲东部的帝王种群以夏末秋末向南迁徙而闻名，从美国北部和加拿大南部迁徙到墨西哥，覆盖数千英里。通过对帝王蝶迁徙过程的简化和理想化，提出了一种新的自然启发的元启发式算法，称为帝王蝶优化算法（monarch-Butterfly Optimization，MBO）。在MBO中，所有的帝王蝶个体都位于两个不同的地方，即。加拿大南部和美国北部（土地1）和墨西哥（土地2）。因此，帝王蝶的位置有两种更新方式。首先，通过偏移算子生成子弹簧（位置更新），并根据偏移率进行调整。然后通过蝶形调整算子调整其他蝶形的位置。为了保持种群不变，使适应度评价最小化，新生成的
English Description：
Emperor butterfly optimization algorithm, a new meta heuristic optimization algorithm! < br / > in nature, the imperial population of eastern North America is famous for migrating southward in late summer and late autumn, from northern United States and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration process of monarch butterfly, a new meta heuristic algorithm called monarch butterfly optimization (MBO) is proposed. In MBO, all monarch butterfly individuals are located in two different places, namely. Southern Canada and northern United States (land 1) and Mexico (land 2). Therefore, there are two ways to update the position of the monarch butterfly. Firstly, the sub spring is generated by the offset operator (position update), and adjusted according to the offset rate. Then the position of other butterfly shape is adjusted by butterfly shape adjusting operator. In order to keep the population unchanged and minimize the fitness evaluation, the new generation of