Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19 - Wang, Mingjing; Chen, Long; Heidari, Ali Asghar; Chen, Huiling (Frontiers in Neuroinformatics, 2023)
Harris hawks optimization (HHO) is a swarm optimization technique that is capable of solving a wide variety of optimization problems. HHO, on the other hand, frequently suffers from insufficient exploitation and a slow rate of convergence for some numerical optimization. To solve this problem, this paper integrates the fireworks algorithm's explosion search mechanism into HHO and presents a framework for fireworks explosion-based harris hawks optimization (FWHHO). More precisely, the suggested FWHHO structure is divided into two search stages: harris hawk search and fireworks explosion search. A search for fireworks explosions is conducted in order to identify suitable places for developing improved hawk solutions. On the CEC2014 benchmark functions, the FWHHO method beats the existing state-of-the-art algorithms. Additionally, 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.