融合正弦余弦算法的蝴蝶优化算法

Butterfly Optimization Algorithm Based on Sine Cosine Algorithm

关键词:  正弦余弦算法  函数优化  蝴蝶优化算法  收敛因子  自适应

Key words:  sine cosine algorithm  function optimization  butterfly optimization algorithm  convergence factor  self-adaption

摘要:

针对蝴蝶优化算法存在收敛速度慢、求解精度差和易陷入局部最优等缺陷，提出一种融合正弦余弦算法的蝴蝶优化算法。首先在蝴蝶自身认知部分引入非线性自适应因子，其次重新定义香味浓度计算公式，最后在局部搜索阶段引入改进的正弦余弦算法。通过19个基准函数的测试，实验结果表明，本算法在收敛速度、寻优精度和鲁棒性方面均优于蝴蝶优化算法（Butterfly Optimization Algorithm，BOA）、正弦余弦算法（Sine Cosine Algorithm，SCA）、樽海鞘群算法（Salp Swarm Algorithm，SSA）、狼群算法（Grey Wolf Optimizer，GWO）和布谷鸟搜索算法（Cuckoo Search Algorithm，CS），与其他改进蝴蝶优化算法相比，在寻优精度方面也具有一定优势。

Abstract:

Aiming at the defects of the butterfly optimization algorithm,such as slow convergence speed,poor searching precision and easy to fall into local optimum,a butterfly optimization algorithm based on sine cosine algorithm is proposed.Firstly,a nonlinear adaptive factor is introduced into the self-cognition part of butterfly.Secondly,the calculation formula of fragrance concentration is redefined.Finally,the improved sine and cosine algorithm is introduced in the local search stage.By testing 19 benchmark functions,the experimental results show that the proposed algorithm is superior to Butterfly Optimization Algorithm (BOA),Sine Cosine Algorithm (SCA),Salp Swarm Algorithm (SSA),Grey Wolf Optimizer (GWO) and Cuckoo Search Algorithm (CS) in terms of convergence speed,optimization accuracy and robustness.Compared with other improved butterfly optimization algorithms,it also has some advantages in optimization accuracy.