Quadratic approximation salp swarm algorithm for function optimization

Prince Solanki () and Kusum Deep ()
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Prince Solanki: Indian Institute of Technology
Kusum Deep: Indian Institute of Technology

OPSEARCH, 2024, vol. 61, issue 1, No 14, 282-314

Abstract: Abstract The Salp Swarm Algorithm (SSA) is a peculiar swarm-based algorithm that is extensively used for solving numerous real-world problems due to its simple structure and effective optimization capabilities. However, like other population-based algorithms, SSA has some limitations, such as poor exploration, slow convergence rate, and poor population diversity. In order to address these limitations, a new approach called the Quadratic Approximation Salp Swarm Algorithm (QA-SSA) is proposed in this study. The QA-SSA utilizes a Quadratic approximation operator-based position update mechanism around the best search agent, which aids the algorithm in escaping local optima and improves its exploratory potential and convergence speed. To evaluate the performance of the proposed QA-SSA, it is compared with several other metaheuristic algorithms such as Aquila optimizer (AO), Arithmetic Optimization Algorithm (AOA), Harris Hawk Optimization (HHO), Grey Wolf Optimizer (GWO), Moth-Flame Optimizer (MFO), Sine–Cosine Algorithm (SCA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA) on IEEE CEC 2017 benchmark functions. The experimental results are analysed using two statistical tests, the Wilcoxon rank-sum test and Friedman statistical test. The results show that the proposed QA-SSA outperforms the classical SSA and other considered metaheuristic algorithms regarding solution accuracy and convergence speed. Moreover, the statistical results also support the superiority of the proposed QA-SSA over other algorithms. It is worth noting that all algorithms are evaluated using the same criteria for fair and effective comparison.

Keywords: Swarm intelligence; Quadratic approximation salp swarm algorithm; Optimization; Wilcoxon rank-sum test; Friedman test (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12597-023-00682-9

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