Abstract

Abstract

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).OPEN ACCESS Received: 19/08/2025 Accepted: 13/10/2025

Abstract

In structural engineering, the primary objective of design engineers is to ensure structural safety under applied loads according to codes and standards while achieving the most economical design. To enhance cost efficiency in complex structures such as reinforced concrete (RC) frames, computational techniques are employed. The aim is to perform a more rapid and accurate optimum cost design of RC frame systems subjected to vertical loads. This study applies five metaheuristic approaches: three metaheuristic methods and two hybrid techniques developed from them. The matrix displacement method is used to determine displacements and sectional forces of RC frame systems, with design rules following ACI 318-19 (Building Code Requirements for Structural Concrete and Commentary). The study models five different RC structures with varying dimensions and member counts, including symmetrical and asymmetrical configurations. Internal forces and displacement values of structures analyzed using the matrix displacement method in MATLAB are verified with SAP2000 structural analysis software, confirming solution validity. Among the methods applied for the optimum design, the TeachingLearning-Based Optimization (TLBO) technique proves particularly suitable for RC frame systems. The proposed design method and TLBO algorithm offer civil engineers a rapid and efficient approach to achieving cost-optimized designs while ensuring structural safety. The developed method helps engineers efficiently solve complex design problems while achieving optimal structural solutions in terms of both cost and safety.OPEN ACCESS Received: 14/08/2025 Accepted: 24/10/2025 Published: 16/04/2026