## 1 Introduction  
Natural disasters are threats to human life and the ecosystem in general. Climate changes as well as environmental changes--e.g., deforestation--increase the frequency and intensity of natural disasters such as hurricanes, floods, and landslides [1]. Such extreme catastrophes cause many losses in lives, affect the economy, and leave many damages to the affected area. However, disaster effects can be reduced if the society is prepared and plans--e.g., for evacuation--are in place. Evacuation is a means to save lives and is incorporated in both preparedness and response phases of disaster operations management [2]. However, evacuation planning is a complex process and more crucial in urban areas due to the high population density and complex urban settlement. A critical challenge in evacuation planning is to find optimum evacuation time and a proper shelter allocation such that they have enough space for evacuees and other basic living requirements. This means that evacuation planning is considered as a complex multi-criteria decision problem with conflicting objectives, constraints, and spatial aspects. Usually, such problems are modeled by multi-objective optimization techniques, which often provide decision-makers with quick responses and reliable solutions to the problem. Various studies reported the complexity of evacuation planning in disaster operations management (DOM) and proposed techniques to solve them [3; 4]. Thus, depending on the type of disaster and according to the aim of emergency planners, evacuation problems have been modeled as network/routing problems [5; 6], transportation problems [7; 8], or location-allocation problems [9; 10; 11]. This study considers evacuation planning as a location-allocation problem. There are two approaches for solving multi-objective optimization evacuation problems: exact methods and metaheuristic methods. The exact methods--such as linear programming, goal programming, mixed-integer programming, and weighted summation--have been widely used for many decades in disaster operations [12; 13]. These methods combine the criteria/objectives of a multi-objective optimization problem with a set of weights provided by decision-makers. Doing so, a single-objective optimization model is created and then a conventional mathematical programming algorithm can be used to solve the problem. [PERSON] and [PERSON] [14] presented a network flow model for lane-based evacuation routing. Mixed-Integer programming was used as a solution method to identify an optimal lane-based evacuation routing plan in a complex road network. Although these old and traditional methods have been widely used to solve multi-objective optimization problems, they have limitations when applied to real-world problems. For example, these techniques are often extremely time-consuming to solve real-world problems with large dimensions, hardly constrained problems, and multimodal problems. In addition, the final solution is highly influenced by, and biased towards, the initial weights provided by experts at the early stage of the algorithm. To overcome these limitations, researchers have used multi-objective optimization instead of single-objective optimization in order to design and solve evacuation problems. Metaheuristic algorithms have the ability to produce good quality solutions in reasonable computation time, good enough for a practical purpose [15; 16]. They are also not biased with the preferences of experts since no initial weighting of criteria is needed. However, not all of these algorithms are efficient, a few algorithms have proved their capacities for solving real-world problems [17; 18]. Moreover, each algorithm has its own limitations. Therefore, it is very important to conduct a comparative study of metaheuristic algorithms on a specific real-world problem. [PERSON] et al. [19] compared two metaheuristic algorithms for location-allocation of earthquake relief centers--genetic algorithm (GA) and bees algorithm (BA).