The gravitational search algorithm (GSA) has been proved to yield good performance in solving various optimization problems. However, it is inevitable to suffer from slow exploitation when solving complex problems. In this paper, a thorough empirical analysis of the GSA is performed, which elaborates the role of the gravitational parameter Gin the optimization process of the GSA. The convergence speed and solution quality are found to be highly sensitive to the value of G. A self-adaptive mechanism is proposed to adjust the value of G automatically, aiming to maintain the balance of exploration and exploitation. To further improve the convergence speed of GSA, we also modify the classic chaotic local search and insert it into the optimization process of the GSA. Through these two techniques, the main weakness of GSA has been overcome effectively, and the obtained results of 23 benchmark functions confirm the excellent performance of the proposed method.

Fuzzy job-shop scheduling problems (FJSPs) with various imprecise factors are a category of combination optimization problems known as non-deterministic polynomial-hard problems. In this paper, a hybrid algorithm HICATS combining discrete imperialist competition algorithm (ICA) and Tabu search (TS) is proposed to solve FJSPs with fuzzy processing time and fuzzy due date. The objective function is maximizing the minimum agreement index, which is on the basis of the agreement index of fuzzy due date and fuzzy completion time. In the proposed algorithm, ICA conducts the global search and TS performs the local search. The imperialist is used to guide the colonies in the same empire. So, local search approach based on TS is applied to the imperialist to perform fine-grained exploitation. The $6 \times 6$ and $10 \times 10$ FJSPs with fuzzy processing time and fuzzy due date are tested to evaluate the performance of the proposed algorithm HICATS in this paper. The highly effective performance of HICATS is shown against the best performing algorithms from the literature. Experimental results demonstrate the advantages of our proposed algorithm HICATS on the feasibility and robustness compared with other algorithms.

Imaging genetics research based on Sparse Canonical Correlation Analysis (SCCA) helps to discover the correlation between pathological features reflected by neuroimaging and genotypic variation. Multi-Task SCCA (MTSCCA) method is to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. However, the MTSCCA method is unsupervised and cannot identify diagnosis-guided genotype-phenotype associations. In order to improve the performance and interpretability of MTSCCA, we propose an improved MTSCCA algorithm, which is a supervised sparse bivariate learning model fused with a linear regression model, in which the regression part plays a guiding role in imaging QT selection. To jointly understand the relationship between genotypes and phenotypes of multiple tasks, in this study, gene expression data and single nucleotide polymorphisms (SNPs) as genetic data are considered in the algorithm. The focus of each task is to determine the genotype-phenotype pattern guided by the diagnostic team to discover the association with SNP/gene and brain region changes. Besides, the Laplacian matrices of three kinds of data are added as prior knowledge to the algorithm penalty item so that the algorithm can analyze the correlation between different features. Compared with other SCCA methods, our algorithm has improved noise resistance and stability, and found some diagnostic-specific SNP/gene-ROI specific to the two diagnostic groups of MCI and AD. Significance: This method provides a way to further study the association of multi-modal biological data and identify the complex association patterns of diseases.

Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer’s disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene co-expression network analysis (WGCNA) with single-cell regulatory network inference and clustering (SCENIC). This enables snRNA-seq data and bulkRNA-seq data to obtain data on the deeper intermolecular regulatory relationships, related genes, and the molecular mechanisms of immune-cell action. In our approach, not only were central transcription factors (TF) STAT3, CEBPB, SPI1, and regulatory mechanisms identified more accurately than with single-omics but also immunotherapy targeting central TFs to drugs was found to be significantly different between patients. Thus, in addition to providing new insights into the potential regulatory mechanisms and pathogenic genes of AD microglia, this approach can assist clinicians in making the most rational treatment plans for patients with different risks; it also has significant implications for identifying AD immunotherapy targets and targeting microglia-associated immune drugs.