A New Model of Projection Pursuit Grade Evaluation Model Based on Simulated Annealing Ant Colony Optimization Algorithm

A New Model of Projection Pursuit Grade Evaluation Model Based on Simulated Annealing Ant Colony Optimization Algorithm

Gai Zhaomei (College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China), Liu Rentao (Department of Municipal and Environmental Engineering, Heilongjiang Institute of Construction Technology) and Jiang Qiuxiang (College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China)
DOI: 10.4018/IJCINI.2018100104

Abstract

Projection pursuit model (PP) is widely used in many fields, especially quality evaluation. One of the biggest shortages of PP was that the projection direction is strongly influenced by relevant parameters. In order to solve this problem, many experts and scholars introduced all kinds of parameters optimization method in PP. Based on the basis of previous studies, the article proposed a new model of projection pursuit grade evaluation model (PPE) integrated with simulated annealing ant colony optimization algorithm (SA-ACO). It provided a new thought and method for quality evaluation research. The case example demonstrated that the accuracy and the effect evaluation of the model was effectively and more objectively and practical in the evaluation of quality.
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2. The Model And Method

2.1. Projection Pursuit Grade Evaluation Model (PPE)

PP was established in 1969 by American scientists Kruskal, which was a valuable new and high technology, used for high-dimensional observation data analysis and processing, was an interdisciplinary field of computer science and technology, statistical theory and applied mathematics, belongs to the frontier today (Q Fu & Zhao, 2006). Through the transformation of higher dimensional data to lower dimensional space, looking for a projection value that can reflect the structure and characteristics of high-dimensional data, so research and analysis the higher dimensional data. It had a lot of advantages, such as strong anti-interference, good robustness, high accuracy, and so on. at present, had been widely applied in prediction (Hassanzadeh, Ebrahimi, Kompany-Zareh, & Ghavami, 2016), classification(Liu, Zhao, Liang, & Wu, 2014), quantitatively evaluate(F. Fang et al., 2016; Pei, Fu, Liu, Li, & Cheng, 2016),and many other areas, a group of gratifying achievements.

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