A seismic fault recognition method based on ant colony optimization

2018 ◽  
Vol 152 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Chen ◽  
Chuangbai Xiao ◽  
Xueliang Li ◽  
Zhenli Wang ◽  
Shoudong Huo
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Recognition Method ◽  
Seismic Fault ◽  
Fault Recognition

Phased Array Radar Signal Recognition Method Based on Ant Colony Optimization and SVM

2012 ◽  
Vol 263-266 ◽  
pp. 2566-2569
Author(s):  
Jian Feng Pu ◽  
Jun Lin ◽  
Yan Zhi Li ◽  
Wei Quan
Keyword(s):  
Ant Colony Optimization ◽  
Phased Array ◽  
Separation Efficiency ◽  
Experimental Results ◽  
Ant Colony ◽  
Radar Signal ◽  
Access Time ◽  
Signal Recognition ◽  
Recognition Method ◽  
Phased Array Radar

In order to improve the efficiency for phased array radar's ESM, an ACO and SVM conjoint method is used in this paper to solve the problem of phased array radar signal recognition. By introducing ACO to supervise SVM parametric selection, the method is able to quickly discover seemly parameter value and improve SVM separation efficiency. Experimental results show that textual algorithm possess upper exactness rate to phased array radar that the whole pulse signals sorting can be identified. With normal-SVM and RST-SVM means to compare, the algorithm SVM parameter access time is short, thereby shorten the monolithic hour.

An Image Recognition Method Based on Ant Colony Optimization Neural Network Using in Imaging Fuze

2011 ◽  
Vol 121-126 ◽  
pp. 1886-1890
Author(s):  
Ke Yong Wang ◽  
Shi Kai Xing
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Image Recognition ◽  
Recognition Algorithm ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Target Image ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Training Time

Target image recognition is an important issue in the information processing of imaging fuse system. In the paper, the main frame is proposed which can solve the problem of target image recognition and many computer simulation experiments are carried out. A recognition algorithm based on ant colony optimization and neural network is proposed. It overcomes the shortcomings of traditional BP algorithm and converges fast. The results of experiments prove that the presented algorithm can shorten the training time effectively and increase the accuracy of recognition, so it is very useful in improving the effective destroying ability of the missile.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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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