Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.

如今，云计算在新兴技术中被称为基于Internet的现代区域，它带来了一种环境，在该环境中，云用户可以基于按使用付费模式租用计算资源，例如硬件，软件，存储等。由于云计算的规模正在广泛扩展，并且云用户的数量也在日益增加，因此，高能耗成为复杂云数据中心运行中的一个严重问题。在这方面，虚拟机（VM）整合在有效利用云资源方面起着至关重要的作用。考虑到多个因素，例如迁移开销，能耗，资源利用率和迁移时间，它将正在运行的VM从过载的物理机（PM）迁移到其他PM。由于VM合并问题被称为NP难题，近年来，已采用了各种旨在解决此问题的自然启发式元启发式算法。但是，在该领域中缺乏系统，详细的调查研究是显而易见的。因此，这种差距促使我们提供当前的论文，旨在突出自然启发式元启发式算法在VM整合问题中的作用，回顾现有方法，对基于重要因素的方法进行详细比较，最后概述未来的方向。