Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang^*, Jinyuan Li, Yang Hu

Journal of Quantum Computing, Vol.2, No.2, pp. 85-95, 2020, DOI:10.32604/jqc.2020.09717

Abstract The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position… More >

Wujie Hu¹, Gonglin Yuan^{1, *}, Hongtruong Pham²

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 787-800, 2020, DOI:10.32604/cmc.2020.02993

Abstract It is well known that Newton and quasi-Newton algorithms are effective to small and medium scale smooth problems because they take full use of corresponding gradient function’s information but fail to solve nonsmooth problems. The perfect algorithm stems from concept of ‘bundle’ successfully addresses both smooth and nonsmooth complex problems, but it is regrettable that it is merely effective to small and medium optimization models since it needs to store and update relevant information of parameter’s bundle. The conjugate gradient algorithm is effective both large-scale smooth and nonsmooth optimization model since its simplicity that utilizes objective function’s information and the… More >

Chia-Ming Fan^1,2, Chein-Shan Liu³, Weichung Yeih¹, Hsin-Fang Chan¹

CMC-Computers, Materials & Continua, Vol.15, No.1, pp. 67-86, 2010, DOI:10.3970/cmc.2010.015.067

Abstract In this study, the nonlinear obstacle problems, which are also known as the nonlinear free boundary problems, are analyzed by the scalar homotopy method (SHM) and the finite difference method. The one- and two-dimensional nonlinear obstacle problems, formulated as the nonlinear complementarity problems (NCPs), are discretized by the finite difference method and form a system of nonlinear algebraic equations (NAEs) with the aid of Fischer-Burmeister NCP-function. Additionally, the system of NAEs is solved by the SHM, which is globally convergent and can get rid of calculating the inverse of Jacobian matrix. In SHM, by introducing a scalar homotopy function and… More >