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

2018 ◽  
Vol 12 (4) ◽  
pp. 69-80
Author(s):  
Gai Zhaomei ◽  
Liu Rentao ◽  
Jiang Qiuxiang
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Projection Pursuit ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
New Model

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.

Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 470-475 ◽  
Author(s):  
Xiang Ying Liu ◽  
Hui Yan Jiang ◽  
Feng Zhen Tang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Trial And Error ◽  
Ant Colony Optimization Algorithm ◽  
Real World Datasets ◽  
Better Than

In this paper ACO (Ant Colony Optimization) algorithm, which is a well-known intelligent optimization method, is applied to selecting parameters for SVM.ACO has the characteristics of positive feedback, parallel mechanism and distributed computation. This paper gives comparison of ACO-SVM, PSO-SVM whose parameters are determined by particle swarm optimization algorithm, and traditional SVM whose parameters are decided through trial and error. The experimental results on real-world datasets show that this proposed method avoids randomness and subjectivity in the traditional SVM. Additionally it is able to gain better parameters which could dedicate to a higher classification accuracy than the PSO-SVM. Results confirm that proposed optimization method is better than the two others.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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