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The improved chaotic seagull optimization algorithm incorporating sparrow search mechanism(PSSOA)was proposed to make breakthroughs in the slow convergence rate, the easiness of trapping into local optimal solutions, and the low computational accuracy of the seagull optimization algorithm. The initial populations were generated by the piecewise linear chaotic map( PWLCM) to evenly distribute individuals in a given space. The non-linear search factor and randomization factor were improved to speed up the convergence and balance the global and local search of the algorithm. The sparrow flight mechanism of sparrow algorithm was better incorporated by PSSOA to expand the individual search range in the later local search stage. The gull position was updated again to help individuals jump out of the local optimal and improve the optimization accuracy. Moreover, the simulation experiments were conducted using 16 benchmark test functions to compare PSSOA with the four meta-heuristic algorithms involving seagull optimization algorithm (SOA), particle swarm algorithm (PSO), gray wolf algorithm (GWO) and marine predator algorithm (MPA), and SOA algorithms improved by other scholars. Then the effectiveness and stability of the algorithms were verified by the Wilcoxon rank sum test. The results show the PSSOA has better convergence speed and solution accuracy compared with other comparison algorithms on about 80% of tested functions and the PSSOA has significant differences with other algorithms proved by the percentage of differences between them reaching 98% in the Wilcoxon rank sum test. [ABSTRACT FROM AUTHOR]

The Euclidean distance is dependent on the local density in the density peaking algorithm. The algorithm is not very effective in dealing with datasets with high-dimensional data and the presence of class clusters with inhomogeneous density. To address these problems, improved density peak clustering combining manifold distance and label popagation (DPC-ML) was proposed. The distance metric in DPC-ML was defined by the manifold distance and forms the manifold distance matrix. And a local density was redefined to fuse the manifold distance with the local density, so that the local density reflects certain local distance information. The experimental data show that the algorithm has good performance in dealing with different shapes and uneven density of class clusters. Moreover, the selection of class cluster centroids was found to be more discriminative in terms of the redefined local density in decision maps drawn by using the DPC-ML algorithm on different artificial data sets. Since a new parameter neighborhood points is introduced, the effect of neighborhood points on clustering results is also explored, and it is found that the clustering index works best when it just becomes a connected graph, further demonstrating that the performance of the clustering results can be improved by the manifold distance. [ABSTRACT FROM AUTHOR]