Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19

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.

Automated summary

Harris Hawks optimization (HHO) is a versatile swarm optimization approach that addresses a wide range of optimization problems. However, it suffers from inadequate exploitation and slow convergence rates in certain numerical optimization scenarios. In this study, the fireworks algorithm's explosion search mechanism is integrated into HHO, resulting in a fireworks explosion-based HHO framework (FWHHO) that successfully overcomes these limitations. Experimental results demonstrate that FWHHO outperforms state-of-the-art algorithms and significantly improves upon existing HHO and fireworks algorithms. Additionally, FWHHO is successfully applied to the diagnosis of COVID-19 using biochemical indices, with statistical evidence indicating its potential as a computer-aided approach for early warning and therapy/diagnosis of COVID-19.