In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform design, is proposed. The differences between the proposed algorithm and the others lie in that PAM and uniform design are firstly introduced to MOPSO. The experimental results performing on several test problems illustrate that the proposed algorithm is efficient.

Single-cell RNA sequencing (scRNA-seq) is emerging as a promising technology. There exist a huge number of genes in a scRNA-seq data. However, some genes are high quality genes, and some are noises and irrelevant genes because of unspecific technology reasons. These noises and irrelevant genes may have a strong influence on downstream data analyses, such as a cell classification, gene function analysis, and cancer biomarker detection. Therefore, it is very significant to obviate these irrelevant genes and choose high quality genes by gene selection methods. In this study, a novel gene selection and classification method is presented by combining the information gain ratio and the genetic algorithm with dynamic crossover (abbreviated as IGRDCGA). The information gain ratio (IGR) is employed to eliminate irrelevant genes roughly and obtain a preliminary gene subset, and then the genetic algorithm with a dynamic crossover (DCGA) is utilized to choose high quality genes finely from the preliminary gene subset. The main difference between the IGRDCGA and the existing methods is that the DCGA and IGR are integrated first and used to select genes from scRNA-seq data. We conduct the IGRDCGA and several competing methods on some real-world scRNA-seq datasets. The obtained results demonstrate that the IGRDCGA can choose high quality genes effectively and efficiently and outperforms the other several competing methods in terms of both the dimensionality reduction and the classification accuracy.

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.