Wang Mingjing, Chen Long, Heidari Ali Asghar, Chen Huiling
School of Computer Science and Engineering, Southeast University, Nanjing, China.
The Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.
Front Neuroinform. 2023 Jan 25;16:1055241. doi: 10.3389/fninf.2022.1055241. eCollection 2022.
Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.
哈里斯鹰优化算法（HHO）是一种群体优化方法，能够处理广泛的优化问题。然而，对于某些数值优化问题，HHO通常存在开发不足和收敛速度缓慢的问题。本研究将烟花算法的爆炸搜索机制融入HHO，并提出了一种基于烟花爆炸的HHO框架（FWHHO）来解决这一问题。具体而言，所提出的FWHHO结构由两个搜索阶段组成：哈里斯鹰搜索和烟花爆炸搜索。进行烟花爆炸搜索以确定可以开发出优越鹰解的位置。在CEC2014基准函数上，FWHHO方法优于目前可用的最先进算法。此外，将新的FWHHO框架与四种现有的HHO和烟花算法进行了比较，实验结果表明FWHHO明显优于现有的HHO和烟花算法。最后，将所提出的FWHHO应用于利用生化指标进化一个用于诊断COVID-19的核极限学习机。统计结果表明，所提出的FWHHO能够区分和分类COVID-19的严重程度，这意味着它可能是一种能够为COVID-19治疗和诊断提供充分早期预警的计算机辅助方法。
