全局优化的蝴蝶优化算法

针对基本蝴蝶优化算法中存在的易陷入局部最优值、收敛速度慢等问题，提出一种全局优化的蝴蝶算法，引入limit阈值来限定蝴蝶优化算法陷入局部最优解的次数，从而改变算法易陷入早熟的问题，结合单纯形策略优化迭代后期位置较差的蝴蝶使种群能够较快地找到全局最优解；将正弦余弦算法作为局部算子融入BOA中，改善迭代后期种群多样性下降的缺陷，加快算法跳出局部最优。在仿真模拟实验中与多个算法进行对比，结果表明改进算法的寻优性能更好。

Butterfly optimization algorithm for global optimization

Aiming at the problems of easy falling into local optimum and slow convergence speed in basic butterfly optimization algorithm, this paper proposed a global optimization butterfly algorithm. It introduced limit threshold to limit the number of times butterfly optimization algorithm falls into local optimum solution, so as to change the problem that the algorithm is easy to fall into premature. It combined simple strategy to optimize iteration. For the butterfly individuals with poor position in the later stage, the population can converge to the global optimum faster. It used the sine-cosine algorithm as a local operator to improve the shortcomings of population diversity decline in the later stage of iteration and accelerate the algorithm to jump out of the local optimum. Compared with several algorithms in the simulation experiment, the results show that the improved algorithm has better optimization performance.