基于交叉迁移和共享调整的改进蝴蝶优化算法

针对蝴蝶优化（monarch butterfly optimization，MBO）算法易陷入局部最优和收敛速度慢等问题，提出了一种基于改进的交叉迁移和共享调整的蝴蝶优化（MBO with cross migration and sharing adjustment，CSMBO）算法。首先，利用基于维度的垂直交叉操作来替换标准MBO算法的迁移算子，形成交叉迁移算子，有效提升其搜索能力；其次，将原始调整算子改为具有信息分享功能的共享调整算子，以加快算法的收敛速度；最后，采用贪婪选择策略取代标准MBO算法中的精英保留策略，减少一次排序操作进而提高其计算效率。为了验证CSMBO算法的优化能力，测试了其在30维和50维函数上的优化，并与三种优化算法进行比较，其实验结果表明CSMBO算法具有良好的优化性能。

Improved monarch butterfly optimization algorithm based on cross migration and sharing adjustment

In order to solve the problems of the monarch butterfly optimization(MBO) algorithm, such as it is easy to fall into local optimum and the convergence speed is low, this paper proposed an improved MBO algorithm with cross migration and sharing adjustment(CSMBO). Firstly, it introduced a dimension-based vertical crossover operation to substitute the original migration operation of MBO, and then generated a cross migration operator. Thus, this operation could improve search ability of MBO algorithm effectively. Secondly, in order to speed up the convergence of MBO algorithm, the sharing adjustment operator with information sharing replaced the original adjustment operator. Finally, this paper utilized the greedy strategy to instead of the elite strategy of MBO, which could reduce one sorting operation and improve the calculation efficiency of MBO algorithm. To evaluate the optimization ability of our CSMBO algorithm, this paper made some experiments on a set of common benchmark functions with 30-dimensions and 50-dimensions, and the results show that the proposed CSMBO algorithm has good optimization performance, and outperforms currently available three optimization approaches.