嵌入翻筋斗策略的自适应秃鹰搜索算法及其应用OACSTPCD

An improved bald eagle search algorithm is proposed to address the problems that the bald eagle search(BES)algo-rithm is easy to slip into local optimum and low solution accuracy.Firstly,a Circle chaotic map is used in place of the original al-gorithm's randomly generated initial population to increase the initial population's diversity.Secondly,in the search selection space phase,adaptive weight is combined to update the bald eagle individual position and balance the search and development ability of the algorithm.Finally,the elite differential variation is fused with a somersault foraging strategy and is used to update the positions generated by bald eagle leader individuals in the subsequent stages.The ability of the algorithm to jump out of local optimum is improved.The method underwent comparative simulation tests in some standard test functions,and the Random For-est classification parameters were optimized using the suggested strategy in this research.The experimental results demonstrate that the improved algorithm outperforms the conventional algorithm in terms of solution efficiency,solution accuracy,and classi-fication accuracy.