Imagine you’re a choreographer putting together a dance routine. You have a group of talented dancers (HHO) who can handle various styles, but sometimes they struggle with coordination and slow progress on specific moves. That’s where the fireworks come in! By incorporating the explosion search from the fireworks algorithm, known for finding exciting new locations, we create a framework called FWHHO that combines the strengths of HHO and fireworks. The FWHHO structure consists of two phases: harris hawk search and fireworks explosion search, ensuring both efficient exploitation and exploration of optimal solutions. And guess what? It turns out that FWHHO outperforms even the most advanced algorithms available in numerical optimization tasks. We compared FWHHO with four existing HHO and fireworks algorithms, and the results were clear: FWHHO reigns supreme. But wait, there’s more! We also discovered that FWHHO can be applied to diagnosing COVID-19 severity using biochemical indices. This means FWHHO could serve as a computer-aided tool for early warning and accurate diagnosis of COVID-19 therapy. Want to delve into the fascinating research? Dive into the full article!
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.
Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.