## 2 Related Work  
The processing of imbalanced data is typically conducted during the preprocessing phase of the modelling process. Since its inception, the Smote algorithm [8] has been a common tool for addressing class imbalance problems in classification tasks. Over the years, various enhancements and evolutions of the Smote algorithm have been proposed in order to improve its effectiveness. [PERSON] et al. [9] enhanced the Smote algorithm by optimising the interpolation process. This involved introducing Gaussian deviation to generate synthetic samples that were closer to the class boundaries. This improved the classifier accuracy in these regions. [PERSON] et al. [10] proposed the Reduced Noise-Smote (RN-Smote) algorithm, which integrates the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify and remove noise within the oversampling process, thus ensuring cleaner training data. [PERSON] et al. [11] proposed a novel parameter-free Smote algorithm that focuses on generating representative synthetic instances to augment the minority class's coverage. This approach expands the decision margin while minimizing noisy insertions. [PERSON] et al. [12] proposed an extension of the Smote approach, namely, the Iterative-Partitioning Filter (IPF), an ensemble-based noise filter that addresses issues arising from noisy and borderline instances in imbalanced datasets through an iterative process. [PERSON] et al. [13] proposed the adaptive robust Smote (RSmote) algorithm, a novel approach that categorises non-noisy minority samples dynamically into boundary and safe samples based on their relative density characteristics. RSmote then synthesises new samples specifically within these boundary and safe areas. The aforementioned work primarily enhances the Smote algorithm's processing capabilities by mitigating the impact of noise. However, the issue of sample distribution is not addressed, resulting in the generation of an excess of samples in areas with a high sample density, which in turn leads to the occurrence of sample overlap. To address these issues, two improvements are made to the Smote algorithm. Firstly, the K-means++ algorithm is employed to cluster minority class samples. Subsequently, the precise number of samples to be generated for each cluster class is determined by calculating the sampling weight of each cluster class. This improves the consideration of the internal structure of the minority class. Secondly, interpolation is performed between the cluster centre and neighbouring points in order to make the generated samples more representative of the overall characteristics. The field of intelligent optimisation algorithms has witnessed significant advancements through the integration of various theories and mechanisms. One noteworthy advancement in this field is the IPSO algorithm [14], which combines the principles of artificial immune system theory with particle swarm optimisation (PSO) [15]. Researchers have sought to enhance the performance of the algorithm by introducing more adaptable parameter adjustment mechanisms and dynamic strategies. [PERSON] et al. [16] proposed a methodology based on IIPSO with dynamic disturbance term (IPSO-DDT). This approach employs genetic algorithm matrix coding for particle representation and integrates an immune information processing mechanism. This combination preserves individual diversity, thereby mitigating premature convergence. In a further contribution to this field. [PERSON] et al. [17] proposed the IIPSO, which dynamically adjusts the scale of sub-populations and incorporates a concentration regulation mechanism, along with an adaptive search range adjustment for vaccination. These modifications serve to prevent the degradation of the population and to enhance both the convergence performance and the global search efficiency. [PERSON] et al. [18] presented an innovative method combining IPSO with the Simulated Annealing (SA) algorithm to facilitate collaborative search operations.