An Artistic Image Segmentation Method Using an Art-Design-Inspiration-Driven Ivy Algorithm

To overcome the limitations of the original Ivy Algorithm (IVYA), including insufficient population diversity, limited step-size adaptability, and premature convergence, this paper proposes a multi-strategy enhanced Ivy optimization algorithm (MEIVYA). The proposed method integrates chaotic population initialization, adaptive growth-rate regulation, and an elite-guided cooperative search strategy to improve global exploration, local exploitation, and convergence stability. Experimental results on the CEC2014 and CEC2017 benchmark suites show that MEIVYA achieves competitive convergence accuracy, robustness, and stability compared with several state-of-the-art metaheuristic algorithms. In addition, MEIVYA is applied to multi-threshold image segmentation based on the Otsu criterion, where it produces clearer segmentation structures and better visual quality. The results demonstrate that MEIVYA is an effective and robust approach for both numerical optimization and artistic image segmentation.