# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
###### Abstract
However, multimodal swarm intelligence algorithms cannot conquer optimization problems caused by global exploration and local exploitation imbalances that deprive late iterations of their required data diversity [19]. The literature provides a wealth of PSO improvements related to this via parameter weight adjustments (e.g., linear decay [20], chaotic dynamism [21], and S-shaped decay [22]), population topologies (e.g., static neighborhood [23], dynamic neighborhood [24], and hierarchical structure particle subswarms [25]), and evolutionary learning strategies (e.g., comprehensive learning strategy [26], generalized opposition-based learning strategy [27], orthogonal learning strategy [28], and dimensional learning strategy [29]). As stated by the no-free-lunch theorem [30], the identification of a singular method that can be universally regarded as superior to all others for every task is not feasible. However, owing to discrepancies between the two aspects of global exploration and local exploitation, multi-swarm techniques can be used to maintain the population diversity needed to facilitate information flows within subpopulations. Hence, heterogeneous multi-subpopulation techniques have become effective in enhancing PSO algorithms [31]. In this study, we propose a novel generalized regression forecasting network (GRFN), that combines kernel ridge regression (KRR) prediction with a new multi-strategy, multi-subswarm PSO (MMPSO)-SVR model to considerably reduce large errors in human body dimension prediction. The resulting highly accurate small-batch, data-driven human body dimension prediction scheme does not require stature percentile anthropometric measurements and 3D body reconstruction. Our hybrid model only needs a few basic body size parameters to obtain detailed body dimensions. For our experiment, we used the processes of [PERSON] et al. [6] to collect lower-body data from 106 women and applied principal factor analysis so that the KRR-based regression model can establish a multi-variate nonlinear correlation map of parameters. This generates preliminary prediction results as a residual sequence that can be used to fit the data linearly. To deal with nonlinear and noisy data, our model takes the residual of the predicted KRR output as input. A residual correction prediction mechanism is employed to improve the fit and predictive performance of our hybrid model, addressing the following aspects: improving prediction accuracy, accounting for unmodeled factors and making adjustments, correcting model bias, and enhancing model robustness. For further optimization, we apply teaching and co-optimization to the MMPSO algorithm, which divides the population into three role-based subgroups: teachers, students, and independent learners. The ability of the MMPSO model to search for the global optimum primarily depends on the diversity of the population. The SVR then balances exploration and exploitation using a diversity-focused multi-strategy search to avoid local optima. Finally, the estimated values of the predicted body parameters are obtained by combining the KRR results with the corrected MMPSO-SVR residuals. Thus, our hybrid model provides verifiable human body dimension predictions that can work with small-batch samples. In summary, this study makes the following contributions:  
1. Our GRFN utilizes a novel KRR-based MMPSO-SVR nonlinear regression network to achieve residual correction prediction. The proposed hybrid model applies a direct approach that utilizes a few basic body measurements to predict other, more detailed human body dimensions from small-batch samples. The results clearly validate the proposed model, as it outperforms state-of-the-art (SOTA) SVR models in terms of both prediction accuracy and reliability. 2. Our MMPSO model adopts a teaching-learning co-optimization scheme in which a teacher subgroup performs enhanced self-learning searching and a student subgroup constructs learning exemplars under the guidance of the teacher group via sub-swarm.