Zou Lewang, Zhou Shihua, Li Xiangjun
Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China.
Entropy (Basel). 2022 Aug 2;24(8):1065. doi: 10.3390/e24081065.
To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.
为了克服哈里斯鹰优化算法（HHO）在探索和利用之间切换时缺乏灵活性以及其利用阶段效率低下的问题，提出了一种高效的改进贪婪哈里斯鹰优化器（IGHHO）并将其应用于特征选择（FS）问题。IGHHO采用了一种新的变换策略，能够在搜索和开发之间灵活切换，使其能够跳出局部最优。我们用改进的差分扰动和贪婪策略取代了原始的HHO利用过程，以提高其全局搜索能力。我们在实验中使用单峰、多峰、混合和复合的CEC2017基准函数与七种算法进行了测试，IGHHO在不同特征函数的优化问题上表现优于它们。我们针对FS中的数据不平衡问题提出了新的目标函数，并将IGHHO应用于该问题。在分类准确率和特征子集长度方面，IGHHO优于比较算法。结果表明，IGHHO不仅适用于不同特征函数的全局优化，也适用于实际的优化问题。
