基于黄金正弦的自适应蝴蝶优化算法

Adaptive butterfly optimization algorithm based on golden sine

针对蝴蝶优化算法存在寻优精度低、易陷入局部最优等问题,文章提出了一种基于黄金正弦算法的自适应蝴蝶优化算法(Adaptive Butterfly Optimization Algorithm with Golden Sine Algorithm,AGSBOA)。首先使用了自适应惯性权重,提高算法跳出局部最优的能力。然后在局部搜索中加入了黄金正弦的搜索策略,提高算法的寻优精度。通过14个函数的仿真实验对比,结果表明优化后的AGSBOA有更好的收敛速度、寻优精度和稳定性。

Aiming at the problems of butterfly optimization algorithm,such as low optimization accuracy and easy to fall into local optimization,this paper proposes an adaptive Butterfly Optimization Algorithm with Golden Sine Algorithm(AGSBOA).First,the adaptive inertia weight is used to improve the ability of the algorithm to jump out of the local optimum.Then the golden sine search strategy is added to the local search to improve the accuracy of the algorithm.The simulation results of 14 functions show that the optimized AGSBOA has better convergence speed,optimization accuracy and stability.