Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design.

Feature selection (FS) for multi-label text classification faces issues such as high dimensionality, strong label correlations, and sparse features, which often lead to suboptimal feature subsets. Moreover, most existing methods are centralized and thus ill-suited to real-world distributed or federated settings, where text data are scattered across multiple nodes and effective FS mechanisms are lacking. To overcome these issues, this paper proposes Fed-MSMCGWO, a federated multi-label text feature selection method based on manifold-aware sparse modeling and cooperative grey wolf optimization. Under a federated learning framework, Fed-MSMCGWO integrates manifold-aware sparse modeling (MSM), and incorporates a cooperative grey wolf optimization algorithm (CGWO) to enable multi-label text FS in distributed environments. On each client, Fed-MSMCGWO employs a two-stage optimization. In Stage 1, MSM is learned by constructing sample and label graphs from text embeddings, encoding their manifolds with Laplacians, and imposing a \(L_{2,1}\)-norm on the feature-weight matrix to induce row sparsity and compress high-dimensional features. In Stage 2, CGWO with a three-line cooperative evolution scheme further refines these weights and conducts global search for a near-optimal subset of text features. After the two-stage optimization, each client obtains a locally optimal feature subset and engages in a multi-party privacy-preserving feature aggregation strategy: clients upload only intermediate feature-weight parameters (no raw data) to the server, which aggregates them and sends the result back to guide further local updates, yielding a collaborative cross-client FS framework with preserved privacy. Experiments on several publicly available multi-label text datasets indicate that, with privacy preserved, Fed-MSMCGWO consistently surpasses standard centralized and federated FS methods on multiple evaluation metrics.

In machine learning, feature selection (FS) is crucial for simplifying data while preserving the variables that most influence predictive performance. Although FS has been extensively studied, addressing it in an unsupervised setting remains challenging. Without class labels, optimization is more prone to slow convergence and the local optima. In particular, unsupervised text FS has received comparatively little attention, and its effectiveness is often limited by the underlying search strategy. To address this issue, we propose a hybrid breeding cooperative whale optimization algorithm (HBCWOA) tailored to unsupervised text FS. HBCWOA combines the cooperative evolutionary mechanism of hybrid breeding optimization with the global search capability of the whale optimization algorithm. The population is partitioned into three lines that evolve independently, while high-quality candidates are periodically exchanged among them to maintain diversity and promote stable, progressive convergence. Moreover, we design an adaptive dynamic accurate probabilistic transfer function (ADAPTF) to balance exploration and exploitation. By integrating the refinement ability of S-shaped transfer functions with the broader search ability of V-shaped ones, ADAPTF adaptively adjusts the exploration depth, reduces redundancy, and improves the convergence stability. After FS, K-means clustering is employed to assess how well the selected features structure document groups. Experiments on the CEC2022 benchmark functions and eight text datasets, under multiple evaluation metrics, show that HBCWOA attains faster convergence, more effective search exploration, and higher clustering accuracy than its S-shaped and V-shaped variants as well as several competitive text FS methods.

Multi-label text feature selection aims to identify compact and discriminative feature subsets for documents associated with multiple correlated labels. In federated environments, this task is further complicated by high-dimensional search spaces, non-independent and non-identically distributed data, complex label dependencies, and the requirement that raw feature and label data remain local. To address these challenges, this paper proposes the Federated Anchor-Guided Frequency-Domain Optimization Algorithm (Fed-AGFDO), a federated multi-label text feature selection framework. At each client, the Anchor-Guided Frequency-Domain Optimization (AGFDO) module transforms the encoded search population into the frequency domain and combines random spectral exploration, elite-guided mixing, diversity-aware anchor screening, and multi-anchor direction aggregation to balance exploration and exploitation. Candidate feature-weight vectors are evaluated using a manifold-regularized fitness function that jointly considers sample structure, label-consensus regularization, sparsity regularization, and a soft global feature-weight consistency penalty. The server aggregates only local feature-weight vectors through sample-size-weighted aggregation followed by an exponential moving average update to smooth inter-round changes in the global feature weights while keeping raw data local. Experiments on eight multi-label text datasets demonstrate that Fed-AGFDO achieves strong classification and label-ranking performance with compact feature subsets. Under the representative subset settings, Fed-AGFDO improves Average Precision by up to $5.83\%$ over Fuzzy Federated Multi-Label Feature Selection (Fuzzy FMFS) and reduces Ranking Loss by up to $22.65\%$ relative to Federated Multi-Label Feature Selection (FMLFS). Parameter sensitivity and ablation analyses further demonstrate robustness across the examined parameter ranges and the complementary contributions of anchor guidance, diversity screening, and multi-anchor aggregation. These results indicate that Fed-AGFDO provides an effective and data-locality-aware solution for high-dimensional federated multi-label text feature selection.