Abstract
This paper introduces a novel bio-inspired metaheuristic algorithm, named the sharpbelly fish optimizer (SFO), inspired by the collective ecological behaviors of the sharpbelly fish. The algorithm integrates four biologically motivated strategies-(1) fitness-driven fast swimming, (2) convergence-guided gathering, (3) stagnation-triggered dispersal, and (4) disturbance-induced escape-which synergistically enhance the balance between global exploration and local exploitation. To assess its performance, the proposed SFO is evaluated on the CEC2022 benchmark suite under various dimensions. The experimental results demonstrate that SFO consistently achieves competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art metaheuristic algorithms. Furthermore, the algorithm is applied to three classical constrained engineering design problems: pressure vessel, speed reducer, and gear train design. In these applications, SFO exhibits strong robustness and solution quality, validating its potential as a general-purpose optimization tool for complex real-world problems. These findings highlight SFO's effectiveness in tackling nonlinear, constrained, and multimodal optimization tasks, with promising applicability in diverse engineering scenarios.