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A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection
International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2021-04-16 , DOI: 10.1007/s13042-021-01326-4
Abdelazim G. Hussien , Mohamed Amin

Harris Hawks Optimization is a recently proposed algorithm inspired by the cooperative manner and chasing behavior of harris. However, from the experimental results, it can be noticed that HHO may fall in local optima or have a slow convergence curve in some complex optimization tasks. In this paper, an improved version of HHO called IHHO is proposed which enhances the performance of HHO by combining HHO with opposition-based learning (OBL), Chaotic Local Search (CLS), and a self-adaptive technique. In order to show the performance of the proposed algorithm, several experiments are conducted using the Standard IEEE CEC 2017 benchmark. IHHO is compared with the classical HHO and other 10 state-of-art algorithms. Moreover, IHHO is used to solve 5 constrained engineering problems. IHHO has also been applied to solve feature selection problem using 7 UCI dataset. The numerical results and analysis show the superiority of IHHO in solving real-world problems.



中文翻译:

具有基于对立面学习和混沌局部搜索策略的自适应哈里斯霍克斯优化算法,用于全局优化和特征选择

哈里斯·霍克斯优化(Harris Hawks Optimization)是最近提出的一种算法,受到哈里斯协作方式和追赶行为的启发。但是,从实验结果可以看出,在某些复杂的优化任务中,HHO可能处于局部最优状态或收敛速度较慢。在本文中,提出了一种改进的HHO版本,称为IHHO,它通过将HHO与基于对立的学习(OBL),混沌局部搜索(CLS)和自适应技术相结合来增强HHO的性能。为了展示所提出算法的性能,使用标准IEEE CEC 2017基准进行了几次实验。将IHHO与经典HHO和其他10种最新算法进行了比较。此外,IHHO还用于解决5个受约束的工程问题。IHHO还被用于使用7个UCI数据集解决特征选择问题。数值结果和分析表明,IHHO在解决实际问题上具有优势。

更新日期:2021-04-16
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