scholarly journals The Application of Ant Colony Algorithms to Improving the Operation of Traction Rectifier Transformers

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 28
Author(s):  
Barbara Kulesz ◽  
Andrzej Sikora ◽  
Adam Zielonka
Keyword(s):  
Ant Colony Optimization ◽  
Supply Voltage ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Dc Voltage ◽  
Ant Colony Algorithms ◽  
Extra Factor ◽  
Tap Changer ◽  
Electric Traction ◽  
Secondary Side

In this paper, we discuss a technical issue occurring in electric traction. Tram traction may use DC voltage; this is obtained by rectifying AC voltage supplied by the power grid. In the simplest design— one which is commonly used—only diode uncontrolled rectifiers are used. The rectified voltage is not smooth; it always contains a pulsating (AC) component. The amount of pulsation varies. It depends, among other factors, on the design of the transformer-rectifier set. In the 12-pulse system, we use a three-winding transformer, consisting of one primary winding and two secondary windings: one is delta-connected and the other is star-connected. The unbalance of secondary windings is an extra factor increasing the pulsation of DC voltage. To equalize secondary side voltages, a tap changer may be used. The setting of the tap changer is the question resolved in this paper; it is optimized by application of the ACO (ant colony optimization algorithm). We have analyzed different supply voltage variants, and in particular, distorted voltage containing 5th and 7th harmonics. The results of ant colony optimization application are described in this paper.

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.

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

A unified ant colony optimization algorithm for continuous optimization

2014 ◽  
Vol 234 (3) ◽  
pp. 597-609 ◽  
Author(s):  
Tianjun Liao ◽  
Thomas Stützle ◽  
Marco A. Montes de Oca ◽  
Marco Dorigo
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm
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