Ant Colony Optimization Based Simulation Analysis of Sports Course Achievement Management System

2021 ◽  
Vol 7 (5) ◽  
pp. 1978-1990
Author(s):  
Mingli Chi
Keyword(s):  
Physical Education ◽  
Ant Colony Optimization ◽  
Management System ◽  
Simulation Analysis ◽  
Rapid Development ◽  
Optimization Method ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
School Teaching ◽  
Scientific Platform

Objectives: With the rapid development of computer technology and network technology, school teaching and management also need to keep pace with the development of the times, and information construction is needed in teaching and management. In the process of campus informationization construction, it is required that all links can be developed in a balanced way, and the school should be built into a scientific platform of information education and teaching from both hardware and software aspects. Methods: Sports achievement management system is based on reducing the workload of physical education teachers, improving teaching efficiency, optimizing the process of students’ class selection, enhancing the identification of students taking part in examinations, saving manpower, financial resources and time compared with the traditional registration model. Results: The ant colony optimization algorithm has achieved encouraging results in solving the combinatorial optimization problem that the traditional optimization method is difficult to work. Therefore, using this algorithm to optimize the simulation analysis of the physical education curriculum management system has better matching characteristics. Conclusion: The system has the characteristics of friendly man-machine interface, easy operation, strong compatibility, fast running speed, etc. It has rich reports and powerful query statistics.

scholarly journals An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ivan A. Mantilla-Gaviria ◽  
Alejandro Díaz-Morcillo ◽  
Juan V. Balbastre-Tejedor
Keyword(s):  
Ant Colony Optimization ◽  
Filter Design ◽  
Optimization Method ◽  
General Purpose ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
X Band ◽  
Waveguide Filters ◽  
Convergence Performance ◽  
Band Pass

A practical and useful application of the Ant Colony Optimization (ACO) method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, respectively, for communication applications, are shown. The average convergence performance of the ACO method is characterized by means of Monte Carlo simulations and compared with that obtained with the well-known Genetic Algorithm (GA). The overall performance, for the simulations presented herein, of the ACO is found to be better than that of the GA.

scholarly journals An improved ant colony optimization algorithm for wire optimization

2020 ◽  
Vol 9 (5) ◽  
pp. 2170-2177
Author(s):  
Ho Joon Jim ◽  
Fazida Hanim Hashim
Keyword(s):  
Ant Colony Optimization ◽  
Simulation Analysis ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Java Programming ◽  
Circuit Delay ◽  
Wire Routing ◽  
Routing Performance ◽  
Complex Circuits ◽  
Cascode Amplifier

Wire optimization has become one of the greatest challenges in today’s circuit design. This paper presents a method for wire optimization in circuit routing using an improved ant colony optimization with Steiner nodes (ACOSN) algorithm. Circuit delay and power dissipation are primarily affected by the length of the routed wire. Thus, the main goal of this proposed algorithm is to find the shortest route from one point to another using an algorithm that relies on the artificial behavior of ants. The algorithm is implemented in the JAVA programming language. The proposed ACOSN algorithm is compared with the conventional ant colony optimization (ACO) algorithm in terms of efficiency and routing performance when applied to three types of circuits: emitter-coupled logic, 741 output and a cascode amplifier. The performance of the proposed method is analyzed based on circuit information such as total wire routing, total number of nets, total wire reduction, terminals per net and total terminals. From the simulation analysis, it is shown that the proposed ACOSN algorithm gives the most benefit to complex circuits, where it successfully reduces the wire length by 21.52% for a cascode amplifier circuit, 14.49% for a 741 output circuit, and 10.43% for emitter-coupled logic circuit.

scholarly journals A multi-objective ant colony optimization method applied to switch engine scheduling in railroad yards

2010 ◽  
Vol 30 (2) ◽  
pp. 486-514 ◽  
Author(s):  
Jodelson A. Sabino ◽  
José Eugênio Leal ◽  
Thomas Stützle ◽  
Mauro Birattari
Keyword(s):  
Ant Colony Optimization ◽  
Time Horizon ◽  
Real Life ◽  
Optimization Method ◽  
Ant Colony ◽  
Operational Planning ◽  
Ant Colony Optimization Algorithm ◽  
Multi Objective ◽  
Operational Policies ◽  
Planning Staff

This paper proposes an ant colony optimization algorithm to assist railroad yard operational planning staff in their daily tasks. The proposed algorithm tries to minimize a multi-objective function that considers both fixed and variable transportation costs involved in moving railroad cars within the railroad yard area. This is accomplished by searching the best switch engine schedule for a given time horizon. As the algorithm was designed for real life application, the solution must be delivered in a predefined processing time and it must be in accordance with railroad yard operational policies. A railroad yard operations simulator was built to produce artificial instances in order to tune the parameters of the algorithm. The project is being developed together with industrial professionals from the Tubarão Railroad Terminal, which is the largest railroad yard in Latin America.

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.

scholarly journals Punching trajectory optimization method for warp-knitted vamp based on improved ant colony optimization algorithm and Radau pseudospectral method

2021 ◽  
Vol 16 ◽  
pp. 155892502110591
Author(s):  
Chi Xinfu ◽  
Li Qiyang ◽  
Zhang Xiaowei ◽  
Sun Yize
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Pseudospectral Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Low Efficiency

Aiming at the problems of complex trajectory, low efficiency and high operational difficulty of the robot in multi-point punching of warp-knitted vamp, a method of optimizing punching trajectory based on improved ant colony optimization algorithm and Radau pseudospectral method is proposed. After obtaining the position coordinates of punching points, an improved ant colony optimization algorithm is used to calculate the punching sequence of the shortest path through all punching points, and then Radau pseudospectral method is used to solve the optimal trajectory of the laser punching robot. Improved ant colony optimization algorithm combines a distributed calculation method and the positive feedback mechanism. Radau pseudospectral method can transform the optimal control problems into nonlinear programming problems, and the combination of the two can quickly and reliably obtain the optimal solution. To verify the method, under the condition of selecting the same number and location of punching points, the experiments of Radau pseudospectral method to solve the trajectory planning of laser punching robot is carried out. The experimental results show that improved ant colony optimization algorithm can calculate the path of the vamp punching point in a shorter time and with high accuracy. Radau pseudospectral method can obtain smooth trajectories satisfying various constraints, which can meet the requirements of accuracy and efficiency in practical production.

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

Sign in / Sign up

Export Citation Format

Share Document