Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.

翻译：自然启发式计算（简称NIC）是一个相对年轻的领域，其通过研究自然现象在复杂问题求解中的运作机制，探索新型计算方法。由此，该领域已在人工免疫系统、神经网络、群体智能以及进化计算等多个方向取得突破性进展。NIC技术广泛应用于生物学、物理学、工程学、经济学和管理学等领域。元启发式算法在现实世界的分类、优化、预测、聚类以及工程与科学问题中展现出高效性与鲁棒性。当前存在两种活跃的NIC范式：引力搜索算法与磷虾群算法。本研究从全球与历史视角综述了磷虾群算法（KH）与引力搜索算法（GSA）在医学与医疗健康领域的应用成果。尽管已有针对其他自然启发式算法的综述研究涉及KH与GSA，但本文首次系统梳理了KH与GSA算法的各类变体及其在医疗健康领域的应用。由于目前缺乏针对医疗领域KH与GSA的专题综述，本文对这两种算法进行了全面回顾，旨在帮助研究人员将其应用于不同领域或与其他主流算法进行混合优化研究。此外，本文从应用场景、改进策略与混合方法三个维度深入剖析了KH与GSA算法。需要特别指出的是，本研究旨在为医疗健康领域的算法研究者提供GSA与KH的算法性能与应用潜力分析视角。