He Ni^1,*, Yongqiao Wang¹, Buyun Xu²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model,… More >

S. M. Mousavi^a, M. Zandieh^b

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 9-16, 2018, DOI:10.1080/10798587.2016.1261956

Abstract This paper considers the problem of scheduling n independent jobs in g-stage hybrid flow shop environment. To address the realistic assumptions of the proposed problem, two additional traits were added to the scheduling problem. These include setup times, and the consideration of maximum completion time together with total tardiness as objective function. The problem is to determine a schedule that minimizes a convex combination of objectives. A procedure based on hybrid the simulated annealing; genetic algorithm and local search so-called HSA-GA-LS are proposed to handle this problem approximately. The performance of the proposed algorithm is compared with a genetic algorithm… More >

Bo Xiao¹, Zhen Wang², Qi Liu^3,*, Xiaodong Liu³

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 365-379, 2018, DOI: 10.3970/cmc.2018.01830

Abstract In recent years, the rapid development of big data technology has also been favored by more and more scholars. Massive data storage and calculation problems have also been solved. At the same time, outlier detection problems in mass data have also come along with it. Therefore, more research work has been devoted to the problem of outlier detection in big data. However, the existing available methods have high computation time, the improved algorithm of outlier detection is presented, which has higher performance to detect outlier. In this paper, an improved algorithm is proposed. The SMK-means is a fusion algorithm which… More >

L. Lamberti^1,2, C. Pappalettere^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.18, No.3, pp. 183-222, 2007, DOI:10.3970/cmes.2007.018.183

Abstract This paper presents a novel optimization algorithm for minimizing weight of skeletal structures. The algorithm--denoted as MPISA (Multi Point Improved Simulated Annealing)--utilizes a multi-level simulated annealing scheme where different candidate designs are compared simultaneously. This is done in purpose to increase computational efficiency and to minimize the number of exact structural analyses.
MPISA is tested in three complicated design problems of skeletal structures: (i) sizing optimization of a planar bar truss under five independent loading conditions including 200 design variables; (ii) sizing-configuration optimization of a cantilevered bar truss including 81 design variables; (iii) sizing-configuration optimization of a frame structure… More >

Sifan Long¹, Ming Zhao^2,*, Xinfu He³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 727-760, 2019, DOI:10.32604/cmc.2019.04454

Abstract With the development of society and the exhaustion of fossil energy, researcher need to identify new alternative energy sources. Nuclear energy is a very good choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material reduced activation ferritic/martensitic (RAFM) steel. The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method (GDM) which combines the gradient descent search strategy of the… More >