摘要
针对人工蜂鸟算法(AHA)在迭代过程中出现全局勘探能力不足和收敛速度较慢的问题,提出了一种多策略改进的人工蜂鸟算法(IAHA)。首先,采用融合Tent混沌映射与反向学习的策略对种群进行初始化,生成高质量的初始种群,为算法全局寻优奠定基础;然后,在引导觅食阶段引入莱维飞行策略以提高全局搜索能力,使算法能够快速跳出局部最优,加快收敛速度;最后,将单纯形法引入算法中,在每一次迭代结束前对质量较差的种群进行处理,提高算法的局部寻优能力。将IAHA分别与4种基本算法、3种单改进阶段的人工蜂鸟算法、2种现有的改进人工蜂鸟算法进行对比,对8个基准测试函数进行仿真实验以及Wilcoxon秩和检验,对IAHA性能进行评估,并分析其时间复杂度。实验结果表明,与上述所提的算法相比,IAHA的收敛速度更快,全局寻优能力更强,算法性能更佳。
To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm(AHA)in the iterative process,a multi-strategy improved artificial hummingbird algorithm(IAHA)is proposed.Firstly,a strategy combining Tent chaos sequence and reverse learning is used to initialize the population,which generates high-quality initial populations and lays a foundation for global optimization of the algorithm.Secondly,the Levy flight strategy is introduced in the foraging stage to enhance the global search ability,enabling the algorithm to quickly escape from local optima and accelerate convergence speed.Finally,the simplex method is introduced into the algorithm to process poorer quality population before each iteration ends,improving the local optimization ability of the algorithm.The IAHA is compared with 4 basic algorithms,3 single-improvement-stage artificial hummingbird algorithms,and 2 existing improved artificial hummingbird algorithms,respectively.Simulation experiments as well as Wilcoxon rank sum tests are performed on 8 benchmark test functions to evaluate the performance of IAHA and to analyze its time complexity.Experimental results show that IAHA converges faster,has better global optimization capability and better algorithmic performance than the above proposed algorithms.
作者
李振
冯锋
LI Zhen;FENG Feng(School of Information Engeineering,Ningxia University,Yinchuan 750021,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S01期88-96,共9页
Computer Science
基金
宁夏重点研发计划重点项目(2022BEG02016)
宁夏自然科学基金重点项目(2021AAC02004)。
