The challenge of finding a global optimum in a solution search space with
limited resources and higher accuracy has given rise to several optimization
algorithms. Generally, the gradient-based optimizers converge to the global
solution very accurately, but they often require a large number of iterations
to find the solution. Researchers took inspiration from different natural
phenomena and behaviours of many living organisms to develop algorithms that
can solve optimization problems much quicker with high accuracy. These
algorithms are called nature-inspired meta-heuristic optimization algorithms.
These can be used for denoising signals, updating weights in a deep neural
network, and many other cases. In the state-of-the-art, there are no systematic
reviews available that have discussed the applications of nature-inspired
algorithms on biomedical signal processing. The paper solves that gap by
discussing the applications of such algorithms in biomedical signal processing
and also provides an updated survey of the application of these algorithms in
biomedical image processing. The paper reviews 28 latest peer-reviewed relevant
articles and 26 nature-inspired algorithms and segregates them into thoroughly
explored, lesser explored and unexplored categories intending to help readers
understand the reliability and exploration stage of each of these algorithms.

本文通过回顾 28 篇最新的同行评审相关文章和 26 种自然启发式算法的应用，对其进行归类以帮助读者了解这些算法的可靠性和探索阶段，并讨论了这些算法在生物医学信号处理和生物医学图像处理中的应用。