嵌入翻筋斗策略的自适应秃鹰搜索算法及其应用  

Adaptive Bald Eagle Search Algorithm Embedded with Somersault Foraging and Application

作　　者：夏煌智 陈丽敏 毛雪迪 祁富 XIA Huang-zhi;CHEN Li-min;MAO Xue-di;QI Fu(School of Mathematical Science,Mudanjiang Normal University,Mudanjiang 157009,China;Institute of Applied Mathematics,Mudanjiang Normal University,Mudanjiang 157009,China;School of Computer and Information Technology,Mudanjiang Normal University,Mudanjiang 157009,China)

机构地区：[1]牡丹江师范学院数学科学学院,黑龙江牡丹江157009 [2]牡丹江师范学院应用数学研究所,黑龙江牡丹江157009 [3]牡丹江师范学院计算机与信息技术学院,黑龙江牡丹江157009

出　　处：《计算机与现代化》2024年第2期7-14,共8页Computer and Modernization

基　　金：黑龙江省自然科学基金资助项目(LH2019F051);黑龙江省高等教育教学改革重点委托项目(SJGZ20200175);牡丹江师范学院研究生科技创新重点项目(kjcx2022-019mdjnu);牡丹江师范学院研究生科技创新项目(kjcx2022-097mdjnu)。

摘　　要：针对秃鹰搜索算法(BES)容易陷入局部最优与求解精度低等问题,本文提出一种改进的秃鹰搜索算法。首先,通过Circle混沌序列取代原始算法中随机产生的初始种群,提升了初始种群的多样性;其次,在算法搜索选择空间阶段中,结合自适应权重对秃鹰个体位置进行更新,平衡算法的搜索与开发能力;最后,利用翻筋斗觅食策略更新后续阶段秃鹰领导者个体位置,并融入精英差分变异增强算法跳出局部最优的能力。在多个标准测试函数进行对比仿真实验,并应用改进算法对随机森林分类参数进行优化,实验结果表明,改进后的算法在求解效率方面有较大提升,且求解精度与分类准确率也优于传统算法。An improved bald eagle search algorithm is proposed to address the problems that the bald eagle search(BES)algorithm is easy to slip into local optimum and low solution accuracy.Firstly,a Circle chaotic map is used in place of the original algorithm’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 Forest 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 classification accuracy.

关 键 词：秃鹰搜索算法 Circle混沌映射 自适应权重 翻筋斗觅食策略 精英差分变异 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]