Search

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems. [ABSTRACT FROM AUTHOR]

Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of dc when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize dc for maximizing accuracy, achieving automatic determination of dc. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC. [ABSTRACT FROM AUTHOR]

In recent years, with the great success of deep learning and especially deep unsupervised learning, many deep architectural clustering methods, collectively known as deep clustering, have emerged. Deep clustering shows the potential to outperform traditional methods, especially in handling complex high-dimensional data, taking full advantage of deep learning. To achieve a comprehensive overview of the field of deep clustering, this review systematically explores deep clustering methods and their various applications. First, the basic network architecture of deep clustering is described in detail, including the common network frameworks, and loss functions. Subsequently, deep clustering is divided into several categories based on the network architecture, and benchmark datasets and evaluation metrics in the field are introduced. Next, the real-world applications of deep clustering are explored in depth, providing successful cases in the fields of bioinformatics, medicine, anomaly detection, and image processing, highlighting the broad applicability of deep clustering in solving real-world challenges. Finally, the paper summarizes its contributions and explores potential directions for future research in deep clustering. • Analyze the theory of deep clustering and its performance advantages and disadvantages. • Summarizes the improvement of deep clustering in recent years, analyzes the improvement effect, and shows it with experimental data. • Finally, the related application research of deep clustering in different fields is introduced. At the same time, we summarize and prospect the improvement and development of deep clustering. [ABSTRACT FROM AUTHOR]