## 1 Introduction  
Earthquakes are considered one of the most destructive natural hazards that cause numerous irreparable physical and financial damage to humans and the environment worldwide [1]. According to the statistics released by the Centre for Research on the Epidemiology of Disasters (CRED, 2015), earthquakes were the third most frequent natural hazard from 1994 to 2013 after flooding and landslide occurrences, resulting in $787 billion of financial damage. Also, during the last two decades, earthquakes and the related disasters, including tsunamis, have been the deadliest natural hazards with a contribution of 55% [2]. Literature reviews [3; 4; 5] have shown that the location, magnitude, and exact time of occurrence of earthquakes are still unpredictable. Therefore, disaster management before earthquakes happen plays an important role in reducing earthquake-induced damage to as low as possible. For this purpose, one of the most efficient strategies is to locate vulnerable sites, structures, and infrastructures so their seismic performance can be improved. In recent years, some researchers have studied the vulnerability of a sample area through investigating the earthquakes that occurred from geotechnical, structural, and socio-economic points of view [6; 7; 8; 9; 10; 11]. It is notable that most of these studies have been conducted based on the information from past earthquakes and their relationship with geo-environmental factors. In these studies, multiple-criteria decision-making (MCDM) methods, including analytic hierarchy process (AHP) [9; 11; 12; 13], fuzzy logic [14; 15; 16], fuzzy-AHP [17; 18], and technique for order of preference by similarity to ideal solution (TOPSIS) [19; 20; 21] in ArcGIS software, have been used. There are multiple studies on the determination of seismic vulnerability that used decision-making methods. However, developing decision-making methods that are capable of easily fulfilling the demands requires expert opinions, and thus, these methods are the main sources of bias and error [22; 23]. The possible shortcomings of these decision-making methods can be eliminated using a proper selection of one or a group of evolutionary and intelligent algorithms. Therefore, these sorts of methods can be integrated with various objective algorithms to reduce the error. For this purpose, many researchers have recently tried to apply artificial intelligence algorithms, such as an adaptive neuro fuzzy inference system (ANFIS) [24; 25; 26] and evolutionary algorithms [27; 28; 29; 30], in geological studies, especially seismic vulnerability assessment, with an emphasis on machine learning algorithms [31]. Although machine learning algorithms are widely used for flood modeling [32; 33; 34; 35; 36; 37; 38; 39; 40], landslide susceptibility mapping [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65], land/ground subsidence [51], wildfire [66], sinkhole [67], drought risk [68], gully erosion [69; 70], and groundwater vulnerability assessment [71; 22; 72; 73; 74], they are rarely used in seismic vulnerability assessment. Among the machine learning algorithms, artificial neural network (ANN) is the most widely used method [75], but due to some drawbacks, such as poor prediction power and requiring big data [35], integration with fuzzy logic was implemented, and finally, the ANFIS algorithm was produced. Although the combination of ANN with fuzzy logic and ANFIS is a hybrid method and has a higher prediction power that both ANN and fuzzy logic method, its results and outputs are dependent on the determination of weights in the membership function [76]. Also, literature review has highlighted that ensemble models have a higher performance than individual models, thus hybrid models are strongly recommended. In the present paper, an integration of three novel and efficient models, involving SWARA, radial basis function (RBF), and teaching-learning-Based optimization (TLBO) methods, was developedin order to assess seismic vulnerability.