柯西变异和自适应权重优化的蝴蝶算法

针对基本蝴蝶优化算法（Butterfly Optimization Algorithm，BOA）存在的收敛精度较低、容易陷入局部最优解的问题，提出柯西变异和自适应权重优化的蝴蝶算法（Cauchy variation and adaptive Weight Butterfly Optimization Algorithm，CWBOA）。通过在全局位置更新处引入柯西分布函数进行变异，在局部位置更新处引入自适应权重因子，改进了蝴蝶算法的局部搜索能力；并且引入动态切换概率p]来权衡全局探索与局部开发过程的比重。改进的算法通过对多个单峰、多峰和固定测试维度的函数进行求解，结果表明，CWBOA对大多数测试函数有更好的求解精度、速度和稳定性。

Butterfly Optimization Algorithm Based on Cauchy Variation and Adaptive Weight

Basic Butterfly Optimization Algorithm（BOA）, which has low convergence precision and easy to fall into the local optimal solution. Cauchy variation and adaptive Weight Butterfly Optimization Algorithm（CWBOA） is proposed. By introducing the Cauchy distribution function at the global location update, the adaptive weighting factor is introduced at the local location update to improve the local search ability of the butterfly algorithm, and the dynamic handover probability p] is introduced to weigh the proportion of the local mining and global search process. This paper improves the algorithm by solving multiple single-peak, multi-peak and fixed test dimension functions. The results show that CWBOA has better solution accuracy, speed and stability for most test functions.