Applications of recent metaheuristics optimisation algorithms in biomedical engineering: a review
Mridul Chawla, Manoj Duhan · International Journal of Biomedical Engineering and Technology · 2014
With the increasing complexity of real-world optimisation problems, researchers from various domains of engineering sciences are constantly looking for accurate, fast and robust optimisers. Over the past few decades, studies on Metaheuristic Optimisation Algorithms (MOA) have shown that these methods can be efficiently used to eliminate most of the difficulties of classical methods. These algorithms have inherent capability to explore a large region of the solution space, are computationally robust and efficient, and can avoid premature convergence. This paper reviews some of the applications of three new algorithms, i.e. biogeography-based optimisation, cuckoo search and bat algorithm, in various domains of biomedical engineering, namely clinical diagnosis, biomedical instrumentation, artificial neural networks, biomedical image processing, bioelectronics, biological control system and biomechanics, and show how these fields have benefitted from the use of these recently introduced MOA based on evolutionary computation.