This study presents the Fishing Boat Group (FBG), a new metaheuristic optimization method based on how fishing boats at sea work together when they hunt for fish. This paper’s main contribution is the development of an algorithm that, through minimal only one parameter tuning, achieves superior computational efficiency for high-dimensional problems while consistently demonstrating robust performance and competitive fitness values across multimodal benchmarks. FBG categorizes boats into three groups (A, B, and C) to search regions near the best boat (G_best), the virtual median boat, and unexplored areas. To check how well it works, FBG was tested against six well-known and newer metaheuristic algorithms using ten multimodal benchmark functions and two real-world engineering design problems. The experimental results show that FBG did better than the other methods in 8 of the 10 tests, suggesting it stays pretty robust and performs consistently across different cases. In practical applications, the algorithm achieved superior mean fitness values for the Three-bar truss (263.896) and Tension/compression spring (0.012989313) designs. Most notably, FBG maintained the shortest execution times across all benchmark functions, with an overall average of 0.68 seconds. These results suggest that FBG could be a strong option for tackling high-dimensional optimization problems, especially in situations where you need both accurate results and fast computation.