## 1 Introduction
### Literature Review  
In the literature, substantial research has focused on the optimization of NMGs, particularly in the context of enhancing resilience. The typical optimization objectives for resilient NMGs include: (1) increasing supply reliability, such as maximizing power restoration [9] and minimizing unserved loads [10]; (2) reducing system costs, including minimizing both economic costs [11] and outage duration [12]; and (3) minimizing power exchange between microgrids [13]. Different approaches have been proposed to address these objectives. To enhance the operational resilience of hybrid NMGs in emergency mode, [PERSON]. [14] employs an incremental cost consensus algorithm to optimize surplus power distribution among microgrids, effectively reducing load shedding. [PERSON]. [15] focuses on restoring more critical loads by optimizing the scheduling of generation and storage resources within NMGs. Additionally, it introduces a measurement-based risk-limiting strategy to manage the intermittency of renewable energy, thereby improving system reliability. To further address challenges in maintaining resilience under extreme conditions, Ref. [16] tackles reactive power management by proposing a two-stage stochastic programming model based on Here-And-Now and Wait-And-See approaches. This model not only considers load shedding costs, but also incorporates the economic operational costs of the NMGs, offering a more comprehensive approach to resilience optimization. However, many of the aforementioned studies focus on optimizing a single or limited set of objectives, often overlooking the role of network reconfiguration in enhancing resilience. This oversight reduces the overall effectiveness of these strategies in fully addressing the complex dynamics of NMGs during extreme events. Over the past few years, the potential of connecting mobile emergency generators (MEGs) and MEs to crucial network nodes during emergency scenarios has also been unveiled by many studies, showcasing a promising approach to alleviating load shedding within power system. For instance, the hierarchical scheduling framework proposed in Ref. [17] incorporates mobile units, repair crews, and distribution feeder reconfiguration. The results show a significant improvement in load supply after the integration of mobile units with the network. Additionally, Ref. [18] introduces a decentralized framework for coordinating the dispatch of mobile power sources and repair crews to restore loads. This framework is formulated as a decentralized partially observable Markov decision process and employs a novel hierarchical and hybrid multi-agent reinforcement learning method to demonstrate its effectiveness in load restoration. To enhance the resilience of active distribution systems, Ref. [19] employs multi-microgrids and mobile energy storage units (MSUs). The study proposes a framework for quantifying resilience and reveals the indispasability of MSUs for restoring critical loads under multiple-fault scenarios. Nevertheless, it is worth noting that the capacity of MSUs is predetermined artificially rather than optimized. Additionally, the obtained optimization solution is based on a single scenario rather than the predicted multiple scenarios. In addition to enhancing system resilience, MES devices can also improve economic efficiency by reducing operational costs. Ref. [20] proposes a novel framework for enhancing energy cooperation within multi-island microgrids using marine MES (MMES). The resultssuggest that MMES can provide substantial economic benefits. Similarly, Ref. [21] optimizes the node locations for MES connections to the grid at different times, leveraging its ability to smooth load fluctuations and facilitate renewable energy integration, which ultimately reduces the operating costs of active distribution networks. In addition to optimizing the operation of MES, Ref. [22] focuses on the deployment of mobile renewable energy charging stations, offering a dynamic and adaptable solution for charging MES devices.