SMBO: a Sinusoidal Modulation Bi-population Optimizer for brain cancer MRI image clustering

This study proposes the Sinusoidal Modulation Bi-population Optimizer (SMBO), which couples a sinusoidal modulation of the step size, jointly driven by iteration progress and individual fitness quality, with dynamic behavior switching (DBS) to achieve a self-adaptive, threshold-free balance between exploration and exploitation. This balance is further reinforced by an asymmetric information flow (AIF) mechanism that sustains diversity between the elite and exploration sub-populations. In simulation experiments on the CEC2017 benchmark suite, SMBO achieves the lowest average rank among six compared algorithms at both D = 10 (1.38) and D = 100 (1.10), and on CEC2022 at D = 10 (1.33). Friedman and Wilcoxon rank-sum tests confirm that these differences are statistically significant. Applied to 4-class brain tumor MRI clustering across three deep feature extraction backbones (ResNet50, VGG16, DenseNet121) combined with PCA, SMBO consistently achieves the best clustering purity, normalized mutual information, and adjusted Rand index among all compared algorithms, reaching a purity of 0.8636, an NMI of 0.6733, and an ARI of 0.6881 with the DenseNet121 backbone. These results establish SMBO as a competitive optimizer for both numerical benchmarks and medical image analysis pipelines.