Network Safety Evaluation of Universities Based on Ant Colony Optimization Algorithm and Least Squares Support Vector Machine

2012 ◽  
Vol 7 (12) ◽  
pp. 419-427 ◽  
Author(s):  
E ying
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Safety Evaluation ◽  
Ant Colony ◽  
Support Vector ◽  
Ant Colony Optimization Algorithm ◽  
Network Safety

Optimization of Milling Parameters for Titanium Alloys Based on Support Vector Machine (Machine Learning) and Ant Colony Optimization Algorithm

2018 ◽  
Vol 770 ◽  
pp. 262-267
Author(s):  
Xiao Xia Zhang ◽  
Zhi Chao Wang ◽  
Hong Yang Chen
Keyword(s):  
Support Vector Machine ◽  
Titanium Alloy ◽  
Ant Colony Optimization ◽  
Learning Algorithm ◽  
Milling Process ◽  
Ant Colony ◽  
Machining Process ◽  
Support Vector ◽  
Ant Colony Optimization Algorithm ◽  
Milling Force

In this paper, the titanium alloy milling process is analysized by finite element method, and the processing quality of this materials will be affected by the milling force. A milling force prediction model was established based on support vector machine (SVM) and ant colony optimization (ACO) for titanium alloy milling process cutting parameters. The main feature of this model design methodology is to hybridize the solution construction mechanism of ant colony optimization (ACO) with support vector machine (SVM) regression based on HYPERLINK "https://cn.mathworks.com/discovery/supervised-learning.html" supervised learning algorithm. The results show that this methodology is very efficient, and thus can be used in the machining process parameters optimum and other material processing fields.

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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