Over the past couple of decades, the research area of network community detection has seen substantial growth in popularity, leading to a wide range of researches in the literature. Nature-inspired optimization algorithms (NIAs) have given a significant contribution to solving the community detection problem by transcending the limitations of other techniques. However, due to the importance of the topic and its prominence in many applications, the information on it is scattered in various journals, conference proceedings, and patents, and lacked a focused-literature that synthesizes them in a single document. This review aims to provide an overview of the NIAs and their role in solving community detection problems. To achieve this goal, a systematic study is performed on NIAs, followed by historical and statistical analysis of the researches involved. This would lead to the identification of future trends, as well as the discovery of related research challenges. This review provides a guide for researchers to identify new areas of research, as well as directing their future interest towards developing more effective frameworks in the context of nature-inspired community detection algorithms.

在过去的几十年中，网络社区检测的研究领域已迅速普及，从而导致了文献方面的广泛研究。自然启发式优化算法（NIA）通过超越其他技术的局限性，为解决社区检测问题做出了重大贡献。但是，由于该主题的重要性及其在许多应用中的突出地位，因此有关该主题的信息分散在各种期刊，会议论文集和专利中，并且缺乏在单个文档中将它们进行综合的集中文献。这篇综述旨在概述NIA及其在解决社区发现问题中的作用。为了实现这一目标，我们对NIA进行了系统的研究，其次是对所涉及研究的历史和统计分析。这将导致确定未来趋势，并发现相关的研究挑战。这篇综述为研究人员确定新的研究领域，以及将他们未来的兴趣转向在自然启发下的社区检测算法的背景下开发更有效的框架提供了指南。