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Clustering of cell types from a large number of high-dimensional heterogeneous cells is a vital step in analyzing single-cell RNA-seq data. Although several computational methods have been proposed to evolve such data, most of them suffer from some limitations such as high-level noise, high dimensionality, and low generalization. To address these challenges, a multiobjective robust continuous clustering algorithm (MORCC) is presented to discriminate the different cell types in a single-cell RNA-seq dataset. Stepwise, first a dimensionality reduction method is applied to map the high-dimensional heterogeneous cells into a desired low-dimensional space while preserving the features of the original space. Then, to overcome the instability of trial-and-error connectivity weights in the robust continuous clustering, MORCC proposes applying evolutionary operators to optimize the connectivity weights dynamically, and to select the suitable parameters with two cluster validity indices. To demonstrate the effectiveness of MORCC, we compare it to several state-of-the-art methods on six single-cell RNA-seq datasets, revealing its superior clustering ability from several perspectives. In addition, we carry out a parameter analysis, a case study, and visualization and biological interpretability analyses to validate MORCC's cell identification capability on single-cell RNA-seq data. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) searching for a moving target is a complex search optimization problem that aims to find a path with maximum probability of observing the target. However, as the area where the target may be located increases in size, existing methods tend to cause easy entrapment into local optima, resulting in poor performance. Hence, we propose a Convex Combination Multiple Populations Competitive Swarm Optimization algorithm (CDCSO) in this paper. First, we suggest applying the multiple population strategy to enhance the search ability of the algorithm. The population of interest is divided into two subgroups that each conduct the CSO. Second, we propose a novel convex combination update strategy to coordinate the two subgroups to search jointly for the global optimal solution. At each iteration, this strategy allows the two subgroups to compete and learn from each other, which prevents the population from falling into the local optimum. We then designed new scenarios containing 3 to 6 probability areas to investigate the performance of our proposed algorithm in multiple and complex scenarios. Results: To demonstrate the effectiveness and stability of the proposed algorithm, we compared it with several swarm optimization algorithms from different perspectives. Experimental results showed that the proposed algorithm has superior performance in following and intercepting a high-probability area. To further demonstrate the generality of the convex combination update strategy, we evaluated the strategy in several developed particle swarm optimization algorithms. The results demonstrated that the convex combination update strategy can be integrated into other algorithms to better address the moving target search problem. [ABSTRACT FROM AUTHOR]