一种混合算子改进帝王蝶优化算法

An Hybrid Operator Improved Monarch Butterfly Optimization Algorithm

帝王蝶优化算法存在收敛速度慢、容易陷入局部最优、求解精度不高等问题,基于此提出一种基于柯西-高斯混合算子帝王蝶优化算法,该改进算法是在标准帝王蝶算法的迁移操作和调整操作中引入柯西-高斯混合算子,并发挥柯西算子全局搜索能力强和高斯算子的局部搜索能力强来改善算法性能,同时也对调整操作中调整率做改进。最后通过数值仿真实验对4个单峰、多峰、混合、复合测函数进行测试,同时与其他算法进行对比,结果表明改进帝王蝶优化算法收敛性、求解精度等性能有所提高,该改进算法适应性、鲁棒性好于其他算法。

For The monarch butterfly optimization algorithm has the problems of slow convergence,easy to fall into local optimum,and low solution accuracy,based on the above,a hybrid operator monarch butterfly optimization algorithm based on Cauchy-Gaussian is proposed,which improves the algorithm by introducing the hybrid Cauchy-Gaussian operator in the migration operation and adjustment operation of the standard monarch butterfly algorithm,and playing the strong global search ability of the Cauchy operator and the strong local search ability of the Gaussian operator to improve the algorithm performance.Finally,20 single-peak,multi-peak,hybrid and composite measurement functions are tested by numerical simulation experiments,and also compared with other algorithms.The results show that the improved emperor butterfly optimization algorithm has improved the performance of convergence and solution accuracy,and the improved algorithm has better adaptability and robustness than other algorithms.