摘要
目的针对秃鹰搜索算法(Bald Eagle Search,BES)在函数优化时存在寻优精度低,易陷入局部最优等问题,提出一种混合策略改进型秃鹰搜索算法(Hybrid Strategy Improved Bald Eagle Search,HSIBES);方法首先利用Logistic映射策略初始化种群,使种群分布更加均匀,其次在搜索空间阶段引入莱维飞行,控制步长,改善收敛效果并跳出局部最优,最后在搜寻空间食物中使用自适应惯性权重,提高收敛速度与精度,平衡算法的局部与全局搜索能力;结果将HSIBES算法与其他五种基准算法以及其他学者改进的算法进行对比,通过在9个测试函数上进行仿真实验,并进行Wilcoxon秩和检验验证HSIBES算法的性能,发现HSIBES的结果优于其他对比算法,与其他对比算法之间具有显著性差异;结论实验结果表明:HSIBES算法的寻优精度,收敛速度以及稳定性都更好,算法的性能更具优越性。
Objective Aiming at the problems of low optimization accuracy and easy fall into local optimization of the Bald Eagle Search(BES)in function optimization,a Hybrid Strategy Improved Bald Eagle Search(HSIBES)was proposed.Methods First,the Logistic mapping strategy was used to initialize the population to make the population distribution more uniform.Secondly,Levy flight was introduced in the search space stage,the step size was controlled,the convergence effect was improved and the local optimal was jumped.Finally,the adaptive inertial weight was used in the search for space food to improve the convergence speed and accuracy and to balance the local and global search capabilities of the algorithm.Results The HSIBES algorithm was compared with other five benchmark algorithms and other improved algorithms by other scholars,and the performance of the HSIBES algorithm was verified by simulation experiments on 9 test functions and Wilcoxon rank sum test,and it was found that the results of HSIBES were better than other comparison algorithms,and there was a significant difference between them and other comparison algorithms.Conclusion The experimental results show that the HSIBES algorithm has better optimization accuracy,convergence speed,and stability,and the performance of the algorithm is superior.
作者
曹慧
秦江涛
CAO Hui;QIN Jiangtao(Business School,University of Shanghai for Science&Technology,Shanghai 200093,China)
出处
《重庆工商大学学报（自然科学版）》
2023年第6期74-82,共9页
Journal of Chongqing Technology and Business University:Natural Science Edition
基金
国家自然科学基金资助项目(72174121).
