摘要
针对传统哈里斯鹰优化算法(Harris Hawks Optimization, HHO)在处理庞杂问题易出现局部最优、收敛速度慢、寻优精度低的缺点,提出一种ELSHHO算法来对其进行改进。首先引入精英反向学习策略来对种群进行初始化,可以有效增强初始种群的多样性;其次在种群位置更新时加入精英反向学习策略可以提高算法探索解空间的能力和解的质量从而降低寻优难度加快收敛速度;最后,通过引入对数螺旋因子来增强算法的局部搜索性能,提高寻优精度。使用具有单峰和多峰特征的10个测试函数来对改进的算法进行验证,通过实验得出,ELSHHO算法可以有效提高收敛速度和寻优精度。
An ELSHHO algorithm is presented to address the drawbacks of classic Harris hawks optimization(HHO)in dealing with complicated problems,such as local optimization,sluggish convergence,and low optimization accuracy.Firstly,elite opposition-based learning approach was used to initialize the population,which can successfully increase the beginning population's variety.Secondly,adding elite opposition-based learning strategy in population location updates improved the ability of the algorithm to explore the solution spaceandthe quality of population,reduce the difficulty of optimization and speed up convergence;Finally,the logarithmic spiral factor was incorporated to enhance the algorithm's local search performance and optimization accuracy.The improved algorithm was verified by using 10 test functions with single peak and multi peak characteristics.The experimental results show that ELSHHO algorithm can effectively improve the convergence speed and optimization accuracy.
作者
唐剑兰
蔡茂国
徐翔
TANG Jian-lan;CAI Mao-guo;XU Xiang(College of Electronics and Information Engineering,ShenzhenUniversity,Shenzhen Guangdong 518000,China)
出处
《计算机仿真》
北大核心
2023年第9期364-370,410,共8页
Computer Simulation
关键词
元启发式
哈里斯鹰优化
对数螺旋
精英反向学习
Metaheuristic
Harris hawks optimization
Logarithmic spiral
Eliteepposition-based learning
