基于混合变异算子的布谷鸟优化算法

An Optimal Cuckoo Search Alogrithm Based on Hybrid Mutation Operator

针对布谷鸟寻优算法在多维优化函数搜索中存在收敛速度慢、寻优精度低的缺陷,提出了一种基于混合变异算子的布谷鸟优化算法。该算法在每次迭代后采用全局收敛引导的非均匀变异算子对鸟窝位置进行变异,再根据最优位置适应度值的变化率确定是否陷入了局部最优值;若陷入局部最优则利用高斯变异算子对鸟窝位置进行调整,从而提高了收敛速度以及寻优精度。通过6个经典测试函数的测试,实验表明改进后的布谷鸟算法具有较好的寻优精度和收敛速度。

The Cuckoo Search （CS） Optimization Algorithm in multidimensional optimization function search is of slow convergence, low accuracy. To improve convergence speed and optimal accuracy, a Hybrid Mutation Operator Cuckoo Search（HMCS） algorithm is introduced. The nest location will be adapted by a non-uniform mutation operator based on global convergence after each iterative process, Gaussian mutation operator will he introduced to adapt the nest location if the last iteration fills into local optimum values. The experimental test result shows that HMCS provides a better optimization precision and convergence rate.