# Adaptive Salp Swarm Algorithm for Optimization of Geotechnical Structures
## 1 Introduction
\begin{table}
**Author, Year** & **Reference** & **Optimization Method** & **Application** \\ \hline [PERSON], 2000 & [19] & Genetic algorithm & Locate the critical circular slip surface in slope stability analysis \\ \hline [PERSON], [PERSON], and & & & \\ [PERSON], 2005 & [20] & Genetic algorithm & Search for critical noncircular failure surface in slope stability analysis \\ \hline [PERSON] et al., 2007 & [1] & Particle swarm optimization & Analyze two-dimensional slope stability \\ \hline [PERSON] et al., 2008 & [11] & Improved harmony search algorithm & Analyze slope stability \\ \hline [PERSON], [PERSON], and [PERSON], 2009 & [21] & Hybrid genetic algorithms & Optimize pile groups \\ \hline [PERSON], [PERSON], and & & & \\ [PERSON], 2009 & [22] & Ant colony optimization & Determine the critical failure surface of the earth slope \\ \hline [PERSON] et al., 2011 & [23] & Modified particle swarm optimization & Optimize design of spread footing and retaining wall \\ \hline [PERSON] and [PERSON], 2012 & [24] & Big bang–big crunch optimization & Optimize design of retaining wall \\ \hline [PERSON] and Assadollahi, 2013 & [25] & Hybrid big bang–big crunch algorithm & Optimize CO\({}_{2}\) and cost of reinforced concrete footings \\ \hline [PERSON] et al., 2013 & [26] & Hybrid firefly algorithm & Multi-objective optimization of foundations \\ \hline [PERSON], [PERSON], and [PERSON], 2013 & [27] & Artificial bee colony algorithm & Locate the critical slip surface in slope stability analysis \\ \hline [PERSON], [PERSON], and [PERSON], 2014 & [12] & Hybrid adaptive gravitational search algorithm & Multi-objective optimization of retaining walls \\ \hline [PERSON], [PERSON], and [PERSON], 2016 & [28] & Imperialistic competitive algorithm & Locate the critical slip surface of earth slope \\ \hline [PERSON] et al., 2016 & [29] & Particle swarm optimization and neural network & Predict seismic slope stability \\ \hline \hline \end{tabular} \begin{tabular}{c c c} \hline \hline \end{tabular} \begin{tabular}{c c} \hline \hline \begin{tabular}{c} \hline \hline \
\begin{tabular}{c} \hline \hline \end{tabular} &
\begin{tabular}{c} Accelerated particle swarm \\ optimization, firefly algorithm, \\ Levy-flight krill herd, whale \\ optimization algorithm, ant lion \\ optimizer, grey wolf optimizer, \\ moth-flame optimization algorithm, \\ and teaching-learning-based \\ optimization algorithm \\ \end{tabular} & Minimize construction cost of shallow foundation \\ \hline [PERSON], 2017 & [30] & Biogeography-based optimization algorithm & Optimize cost of retaining wall \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Application of metaheuristic algorithms for geotechnical engineering problems. A new meta-heuristic algorithm called the salp swarm algorithm (SSA) simulates salp fish swarming in deep waters [49]. Section 2 contains more information on the SSA's motivation and mathematical modelling. The SSA in its basic model can be extended or hybridized with another algorithm to produce better answers for future problems, similar to other metaheuristic approaches [14; 43; 50]. This paper presents an adaptive salp swarm algorithm (ASSA) for optimization by introducing new position-updating equations for leader and follower salps. This change significantly improves the algorithm's performance and convergence speed. A set of well-known standard benchmark functions from the literature is used to validate the