Swift Flight Optimizer: a novel bio-inspired optimization algorithm based on swift bird behavior

Metaheuristic algorithms inspired by natural phenomena have become indispensable tools for addressing complex, high-dimensional, and multimodal optimization problems. Nevertheless, many existing approaches are constrained by premature convergence, stagnation, and inadequate balance between exploration and exploitation, thereby limiting their effectiveness in solving challenging benchmark problems. This study introduces the Swift Flight Optimizer (SFO), a novel bio-inspired optimization algorithm grounded in the adaptive flight dynamics of swift birds. The novelty of SFO lies in its biologically motivated multi-mode framework, which employs a glide mode for global exploration, a target mode for directed exploitation, and a micro mode for local refinement, augmented with a stagnation-aware reinitialization strategy. This design ensures sustained population diversity, alleviates premature convergence, and enhances adaptability across high-dimensional search landscapes. The efficacy of SFO was rigorously assessed using the IEEE CEC2017 benchmark suite. Experimental findings reveal that SFO attained the best average fitness in 21 of 30 test functions at 10 dimensions and 11 of 30 test functions at 100 dimensions, thereby exhibiting accelerated convergence and a robust exploration-exploitation balance. Comparative evaluations against 13 state-of-the-art optimizers, including PSO, GWO, WOA, and EMBGO, further demonstrate the superior performance of SFO in terms of convergence speed, solution quality, and robustness. Collectively, these results establish SFO as a novel and competitive metaheuristic framework with significant potential for solving large-scale, multimodal, and high-dimensional optimization problems.