Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

By Seyedali Mirjalili, Amir H. Gandomi, Seyedeh Zahra Mirjalili, Shahrzad Saremi, Hossam Faris. Advances in Engineering Software, 2017

4,481 citations recorded by the source corpus.

A novel optimization algorithm called Salp Swarm Optimizer (SSA) is proposed.Multi-objective Salp Swarm Algorithm (MSSA) is proposed to solve multi-objective problems.Both algorithms are tested on several mathematical optimization functions.Two challenging engineering design problems are solved: airfoil design and marine propeller design.The qualitative and quantitative results prove the efficiency of SSA and MSSA. This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2096fc288ef32942b52602fc671a8fc1bca5c001

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Citations in the corpus

Prairie Dog Optimization Algorithm (2022, methodology): “It can be observed that most of the algorithms returned the optimal solution; however, the PDO is second to both SSA and GWO, which returned the least average weight value compared to the other competing algorithms.”
Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems (2020, background): “…researchers due to their efficient problem solving ability; such as, Grey wolf optimization (GWO) [16], Harris’ hawks optimizer (HHO) [4], bacterial foraging optimization (BFO) [17], moth-flame optimization (MFO) [18], salp swarm algorithm (SSA) [19], and whale optimization algorithm (WOA) [20].”
Optimization Method for Forecasting Confirmed Cases of COVID-19 in China (2020, methodology): “Moreover, SSA is also an optimization algorithm proposed by Mirjalili et al. [48] inspired by the behavior of salp chains.”
A survey on new generation metaheuristic algorithms (2019, methodology): “Mafarja and Mirjalili (2017) propose two hybrid WOA for feature selection where they combined SA with WOA.”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2013) 45 Artificial Searching Swarm Algorithm (ASSA) Chen et al.”