Freshwater snail optimizer: a bio-inspired optimizer for engineering design problems

• Gao-Yuan Liu ^[3] ; Jeng-Shyang Pan ^[1] ; Shu-Chuan Chu ^[1] ; Ru-Yu Wang ^[2] ; Bo-Xiong Yang ^[4] ; Ying Yu ^[3] ; Seyedali Mirjalili ^[5]
  1. [1] Nanjing University of Information Science and Technology

     Nanjing University of Information Science and Technology

     China

     
  2. [2] Shandong University of Science and Technology

     Shandong University of Science and Technology

     China

     
  3. [3] School of Information and Intelligent Engineering, University of Sanya, Sanya, 572022, China
  4. [4] Hainan Environmental Big Data and Digital Governance Key Laboratory of Philosophy and Social Sciences, University of Sanya, Sanya, 572022, China
  5. [5] Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 825001, Australia

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• Localización: Métodos numéricos para cálculo y diseño en ingeniería: Revista internacional, ISSN 0213-1315, Vol. 41, Nº 4, 2025
• Idioma: inglés
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• Resumen

  • As the scale and nonlinearity of optimization problems continue to increase, traditional deterministic solution strategies are becoming increasingly flawed in the face of the exponential growth of search space dimensions and multimodal objective functions. Metaheuristic algorithms, with their probability-driven global search capabilities and local development capabilities, have gradually become an essential tool for solving complex optimization tasks. We propose a Freshwater Snail Optimizer (FSO), inspired by the social behavior of water snails in terms of movement and collision, as a metaheuristic algorithm. FSO combines the floating of water snails’ air chambers, movement in the water, and population collisions and divides them into groups during initialization to balance exploration and development, achieving gratifying optimization results, especially in high-dimensional problems. We utilized CEC 2017 and CEC 2022 to qualitatively analyze FSO in various problems, and employed the Friedman test and Wilcoxon rank sum test for statistical testing. Experimental results show that our proposed FSO achieved 32 first-place results on 41 problems compared with 9 classic algorithms, and 27 first-place results when compared with 9 emerging algorithms that appeared in the past two years. FSO has also achieved the first comparison results in six engineering optimizations on multiple occasions, proving that FSO possesses well optimization capabilities and practicality for real-world problems. The source code accompanying this article has been released at:https://github.com/leogalaxy0603/Freshwater-SnailOptimizer(accessed on 12 October 2025)