# Adaptive Salp Swarm Algorithm for Optimization of Geotechnical Structures
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author, Year** & **Reference** & **Optimization Method** & **Application** \\ \hline \multirow{3}{*}{[PERSON] et al., 2017} & \multirow{31}{*}{[31]} & Genetic algorithm, differential evolution, & \\  & & evolutionary strategy, and & \\  & & biogeography-based optimization & \\ \hline [PERSON] et al., 2017 & [32] & Monte Carlo simulation technique & Assess safety of slope \\ \hline [PERSON], [PERSON], and [PERSON], 2017 & [2] & Interior search algorithm & Optimize retaining wall \\ \hline [PERSON] et al., 2019 & [33] & Hybrid imperialist competitive & Predict safety factor values of retaining \\  & & algorithm and artificial neural network & walls \\ \hline \multirow{3}{*}{[PERSON] et al., 2019} & \multirow{34}{*}{[34]} & Imperialist competitive algorithm, & \\  & & genetic algorithm, particle swarm & Predict slope stability under static and \\  & & optimization, and artificial bee colony & dynamic conditions \\  & & combined with artificial neural network & \\ \hline [PERSON] et al., 2019 & [35] & Neural network system & Design retaining wall structures based \\ \hline [PERSON] et al., 2019 & [36] & Hybrid artificial neural network and ant colony optimization & Assess dynamic conditions of retaining \\ \hline [PERSON] and [PERSON], 2019 & [37] & Particle swarm optimization & Determine critical failure surface \\  & & considering seepage and seismic loading \\ \hline [PERSON] et al., 2020 & [38] & Grey wolf optimization algorithm & Optimize retaining walls \\ \hline [PERSON], [PERSON], and & [39] & Eleven metaheuristic algorithms & Optimize design of cantilever retaining \\  & & & walls \\ \hline [PERSON] et al., 2020 & [4] & Differential algorithm, evolution & \\  & & strategy, and biogeography-based & \\  & & optimization algorithm & \\ \hline [PERSON], [PERSON], and [PERSON], 2021 & [40] & Hybrid butterfly and symbiosis & \\  & & organism search algorithm & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON], 2021} & \multirow{3}{*}{[41]} & Black hole mechanics optimization, & \\  & & firefly algorithm, evolution strategy, and & \\  & & sine cosine algorithm & \\ \hline [PERSON], 2021 & [42] & Teaching–learning-based optimization & Optimize retaining wall \\ \hline [PERSON] and [PERSON], 2021 & [43] & Improved salp swarm algorithm & Locate critical slip surface of slopes \\ \hline [PERSON], [PERSON], & [44] & Hybrid tunicate swarm algorithm and & \\  & & pattern search & \\ \hline [PERSON] et al., 2022 & [45] & Adaptive tunicate swarm algorithm & \\  & & & emissions of shallow foundation \\ \hline [PERSON], [PERSON], & [46] & Artificial neural network combined with & Predict the ultimate bearing capacity of \\  & & rat swarm optimization & shallow foundations and their optimum \\  & & design \\ \hline [PERSON], [PERSON], et al., 2022 & [47] & Adaptive sperm swarm optimization & \\  & & under seismic load \\ \hline \multirow{3}{*}{[PERSON] et al., 2022} & \multirow{3}{*}{[48]} & Multi-objective particle swarm & \\  & & optimization, multi-objective multiverse & \\ \cline{1-1}  & & optimization and Pareto envelope-based & \\ \cline{1-1}  & & selection algorithm & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._effectiveness of the proposed approach. Furthermore, numerical geotechnical structure optimization tests are used to investigate the proposed method's performance and efficiency.