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2016 International Congress on Computation Algorithms in Engineering (ICCAE 2016) ISBN: 978-1-60595-386-1

1 RESEARCH BACKGROUND

Education is always one of the key factors in the de-velopment of the country. The course education sys-tem makes a major breakthrough in the reform of ed-ucation, but this platform is not very mature. In the process of establishing the new course education sys-tem and integrating with the advanced science and technology and elements, its construction process is very complex.

The ant colony algorithm has its strength in dealing with the optimal choice of the complex issues, which has been widely used since its proposal. The ant colo-ny algorithm has been used in macolo-ny issues, such as truck freight, postman delivery, supplier’s supply route, course arrangement, sorting of the complex network structure and so on.

Robot is another breakthrough in the technological development in China, which also applies for the ant colony algorithm. The robot movement issues repre-sent the level of its development value. How to design one of routes that is the most suitable for the robot movement requires to reasonably planning its routes. The introduction of the ant colony algorithm helps the developers to find out a dynamic route from many routes, which can not only meet the requirements of the kinematic design of the robot, but also preferably perform its motor skills.

In the reform of education, the ant colony algorithm

is also applicable. Among them, more prominent issue is the course arrangement for the students. The train-ing programs in each major in the school are very different, so the course arrangement is very crucial. The ant colony algorithm can arrange a set of the most suitable teaching program under the premise of meet-ing the requirements of trainmeet-ing.

The ant colony algorithm is also applied to establish the course education system. It is very reasonable to plan and design the substructure of the course educa-tion system by the use of the ant colony algorithm, and screen the structural composition which is the most conductive to the construction of the system platform. For this reason, this paper proposes an idea of estab-lishing the course education system by the use of the ant colony algorithm. This idea can be used to inte-grate data of various disciplines and fields into a sys-tem platform and link up with the learners on this platform around the world, thus establishing a far- reaching learning system to achieve the objective of learning and exploration anytime and anywhere, and make and share progress together.

2 RELEVANT RESEARCH

2.1 Ant colony algorithm and its application

In nature, ant is one of the most common insect, and

Applied Research and Implementation of Ant Colony

Algorithm in the Course Education System

Wei Jiang, Jingjia Qi & Yang Wang

Harbin Finance University, Harbin, Heilongjiang, China

ABSTRACT: This paper implements the establishment of a new course education system based on the ant colony algorithm. Through the research of the ant colony algorithm and its application and the current status of the course education system, the authors find that the current structure of the course education system is still not perfect, and the system content is also simple. The ant colony algorithm is used to screen the structural composi-tion and influence factors that are the most conducive to the construccomposi-tion of the system platform and eventually build an optimum structure of the course education system, which is another application for the ant colony algo-rithm in the field of education.

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its crawling route has certain characteristics. Ant is a compound animal. In the process of group action, there is a certain relation between each ant. The for-aging route can be found out through the biological information released by the animal, which is the shortest one of all routes. For this reason, inspired by the biology, this rule will be evolved into the mathe-matics.

(a) Ants crawling route just find food sources

[image:2.516.76.224.150.299.2]

(b) Ants crawling route after a period of time

Figure 1. Ant colony foraging schematic.

Figure 1(a) shows that the ants arrive at the destina-tion along different routes after finding food. The ant crawling route will be shifted into Figure 1(b) after a period of time. That is to say, basically all the ants will crawl along the shortest route to arrive at the destina-tion. In our real life, this rule will be used for solving a lot of optimal solutions.

The ant colony algorithm is the most widely-used to solve the issues of route planning. The ant colony algorithm can be used to solve the problem of the shortest route, and many other issues in real life, such as truck freight, postman delivery, supplier’s supply route and so on.

In addition to the routing problem, the ant colony algorithm can also be used to solve the optimal solu-tion of the problem. In the educasolu-tion of students, the educational administrations are often troubled by the problem of the course arrangement. Due to the differ-ence of training programs between different classes, teachers, students, grades and the intersection and difference between the courses, it is very difficult to arrange courses.

In the contemporary network society, it aggregates various problems in real life into the virtual network, causing that the complexity of the network is as much as any problems in life. To sort and classify the com-plex network structure by the use of the ant colony algorithm greatly embodies the advantages of the ant colony algorithm.

2.2 Course education system

The traditional education is the current more common education way. The teaching method is to impart

knowledge in this mode since the development of education in China. With the continuous progress of the times, the reform of education is particularly im-portant. The key to the reform of education is to con-tinuously learn the advanced science and technology for teaching and improve the teaching quality.

For the contemporary reform of the course educa-tion, there is a must to adhere to the advantages of the traditional education and introduce the contemporary advanced science and technology on this basis. The computer technology and network technology are important factors to promote the reform of course education. In this regard, the course education system proposed based on the contemporary advanced net-work technology should have following advantages:

(1) The students can freely access to learning re-sources: By the use of the platform of the course edu-cation system, the students can login and download the data that are suitable for learning after registering an account. This system is not limited to the school roll of the students, and all data are free for you if you have already registered;

(2) The time and number of use is not limited: The course education system is beneficial to a vast number of learning fans, and the learners can login to down-load data at any time, and the number of use is not limited, and the number of courses is free of require-ment. You can attend a lecture online if you login to select the course, so as to avoid the problems of the resources of teachers and students caused by the classroom and other external factors in the traditional courses;

(3) Open course information: A variety of learning course information, learning goals, public resources and other issues that are not involved in the privacy and jurisdiction will be disclosed on the platform of the course education system.

3 ALGORITHM DESIGN AND IMPLEMENTATION

Through the above brief introduction of the ant colony optimization algorithm, we have an initial under-standing of its mechanism. In the process of estab-lishing the course education system, the ant colony optimization algorithm can be used to more clearly describe this process and eliminate unnecessary fac-tors to find the optimum structure to construct this system. Therefore, we now build an ant colony system model in regard to the above process.

First, the meaning of the following symbols is in-troduced as follows: n is the number of substructures required to build the system; m is the number of ants in the ant colony; dijis the degree of correlation

be-tween the substructures i and j; bi(t)is the number of ants in the place of i at the moment of

t

;

n

i tt

b m

1

)

(3)

) (t

ij

 is the track strength (namely, the residual amount of information in the lines of ij) of the edge arc (i, j) at the moment of t; assuming that ij(0)c (c is a constant), i, j=1,2,,n,ij; ij(t)is the

transport degree of the edge arc (i, j) at the moment of t, representing the degree of expectation from the place i to the place j. Based on the above principle, the ants k(k=1,2,,m) transfer the direction in the motor process according to the amount of information on each path. Different from the ant colony crawling in nature, the artificial ant colony system constructed now can record crawling data. After a period of time, that is, after the moment of n, the marks of crawling route, namely, the information remained by the ants will gradually vanish. After completion of each cycle, these marks will be re-adjusted. Thus:

(1) Assuming that the artificial ant colony finds an optimal solution in parallel, the specific media can be regarded as the basis to look for the next node until finding out the optimum substructure grouping to construct the course system;

(2) The transition probability of the artificial ants k from the place i to the place j at the moment of t is:

        

 , 0 , ) ( ) ( ) ( ) ( ) ( s j s j t t t t t p s v iv a iv iv a iv k ij       (1)

(3) When martificial ants find the optimum feasi-ble solution in accordance with the formula in (2), the amount of information on each side can be modified by the following formula, namely, the equation turns into:

         m k k ij ij ij ij

ij t t

1 ) 1 , 0 ( , ) ( ) 1 (       (2)

For the course education system, there are more substructures in its system composition, and the fac-tors that affect the use effect of the system are more complex, so it belongs to the complex optimization problem. Therefore, unnecessary influence factors and substructures are eliminated through the ant colony optimization algorithm, in order to achieve the con-struction of the optimal system platform.

The course education system achieves the learning of various disciplines and high-end knowledge in var-ious fields and the exchange between talents, which is applicable for the students learning, expert training, and exchange between various disciplines.

The main structure of the course education system mainly includes sum of resources, combination of resource information, customized learning goals, feedback and response. The system is not very mature, which requires to be improved by introduction of many technologies for each component in the system.

Therefore, the ant colony algorithm can be used to screen different sciences and technologies and influ-ence factors, and it is very crucial to construct an op-timal system structure.

4 SIMULATION AND EXPERIMENT

The key of a country is the development of the history of education. The educational level is one of indicators to evaluate the future prospect of a country. The re-form of the course education system provides a con-venient platform for learning so that more people in-cluding students can learn anytime and anywhere.

The country is progressing, and the education is al-so reformed constantly. Free-type teaching gradually appears since the traditional teaching, which encour-ages the teamwork of students and promotes the en-thusiasm for learning among students in the form of competition. For multimedia teaching, it is a kind of teaching method appeared after the network construc-tion integrating into life, which is the further progress of the reform in education.

In China, currently, there are six teaching modes, mainly including the basis and group-based teaching, general theory-based teaching, collaborative teaching, race against type teaching, situation into type teaching (to set certain scenarios for the teaching tasks), and practice oriented teaching.

For the design of the course education system, there is a need to consider the use of the proportion of stu-dents and teachers in the system, so there is a must to do interaction design of the relevant teaching. There are two kinds of basic design methods for this matter. One is to give a number of responses to a large num-ber of interaction and questions in the course educa-tion system by the use of the network platform, and the other is to carry out the classroom testing and chapter assessment and test in the online assessment system. For the main structure of the course education system, its main contents are as follows:

Sum of resources: A large number of learning ma-terials are summarized to the resource library of the course education system through the network platform, and these materials can be spread on different web-sites in the form of a web browser, so the learners in different areas and different circles are capable of learning and ultimately achieve the purpose of exten-sive browse.

Combination of resource information: All teaching materials in the relevant courses are reorganized to enrich the content of the teaching materials.

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Feedback and respond: The platform of the course education system can achieve learning exchange be-tween different areas, and also achieve exchange and sharing between learning fans in different countries, feedback and respond to the questions proposed in the system, thus achieving the optimal state through stant arrangement and development of the course con-tent of the system.

On the above basis, different teaching modes are integrated into the main structure of the current course education system, and the optimum structure can be solved through the artificial ant colony method. The process is as follows:

First step: NC←0 (NC is the step number of itera-tion and the number of search); initializaitera-tion ofijand

ij

 ;

Second step: The starting point of all ants is re-quired at the same level; the ants k(k=1,2,,m) crawl to the next place j, and the transition probability ispkij.

Third step: Calculate Zk(k=1,2,,m) and record the

current optimal solution;

Fourth step: Modify the track strength in accord-ance with the formula in (1).

Fifth step: NCNC+1, if the same solutions are

found out, then return to the second step.

When the route of data delivery is more complex, its calculation process is very troublesome, so:

t 1

ij

  

t 1

ij

 

01 ij      

(3)

Where:

    

   

 

, 0

ij , d

1

ij k ij

BE

 (4)

Thus, there is a need to consider the collaborative teaching, race against type teaching, situation into type teaching and practice-oriented teaching in the estab-lishment of a new course education system. Different learning plans and objectives are designed for differ-ent studdiffer-ents in order to improve the academic achievement and ability from different levels, thus establishing the optimal structure of the new course education system as shown in Figure 2.

[image:4.516.57.448.357.653.2]

The establishment of the course education system achieves the application for the ant colony algorithm in education and teaching, which not only provides direction for the reform of the course education, but also is another breakthrough of the ant colony algo-rithm in the application.

Table 1. Statistics of calculation results.

Type Basis and group-based teaching

General theo-ry-based teaching

Collaborative teaching

Race against type teaching

Situation into type teaching

Practice oriented teaching

Course education

Weight 9.5% 8.3% 26.7% 27.1% 25.8% 22.6% 48.92%

Total 17.8% 53.8% 48.4% 48.92%

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5 CONCLUSION

This paper analyzes the applied research and status quo of the ant colony algorithm in the course educa-tion system, introduces the ant colony algorithm, and carries out the relevant research of the course educa-tion system, and finally finds out the problems of the current course education system. The ant colony algo-rithm is coupled to the development of the course education system, and also used to finally achieve the construction of the new structure of the course educa-tion system through simulaeduca-tion and experiment.

As a genetic algorithm, the ant colony algorithm can deal with complex data issues and has a wide range of applications, which can be used for the de-velopment of the course education system, so the da-tabase is enriched and the spread scope becomes wider. The new course education system integrates into the course knowledge systems in various areas, and achieves the exchange between learning fans in ferent areas, different circles of intellectuals and dif-ferent world scales.

Constructing the structure of new course education system based on the ant colony algorithm has a con-siderable application prospect. In the future, it will be applied to develop a variety of courses and establish the students’ knowledge system. In addition, this algo-rithm can also be applied to construct other exchange platforms.

ACKNOWLEDGEMENT

This paper is financially supported by the Key Project of Science Planning of Heilongjiang Province, Chi-na—Reform and Research on the Teaching Methods of Computer Basic Education in Universities under the Background of Internet+ (GN: GJB1215032).

REFERENCES

[1] M. Ghaedi. 2014. The optimization of gas allocation to a group of wells in a gas lift using an efficient ant colony algorithm (ACO). Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[2] Liu Changan & Yan Xiaohu. 2011. Planning method of dynamic route of mobile robots based on improved ant colony algorithm. Journal of Electronics.

[3] Sukon Kan chanaraksa & Ira Gooding. 2009. Ohns Hop-kins Bloomberg School of Public Health OpenCourse- Ware. Open Learning: The Journal of Open and Dis-tance Learning.

[4] Liu Weihong. 2003. Analysis of Holographic Theory of Physical Education Course System. Chongqing: South-west Normal University.

[5] Tsong-Liang Huang & Barrett. 2008. Upper core point detection using improved ant colony optimization algo-rithm. Journal of Discrete Mathematical Sciences and Cryptography.

[6] Jin Di & Yang Bo. 2012. Detection of cluster structure of complex networks - based on random walk ant colony algorithm. Journal of Software.

[7] A. M. Mora. 2013. Pareto-based multi-colony mul-ti-objective ant colony optimization algorithms: an island model proposal. Soft Computing.

[8] Claudio Iacopino. 2012. The dynamics of ant colony op-timization algorithms applied to binary chains. Swarm Intelligence.

[9] Wang Peidong. 2012. Applied Research of Improved Ant Colony Algorithm and Route Planning. Qingdao: China Ocean University.

[10] P. Udhayakumar. 2010. Sequencing and scheduling of job and tool in a flexible manufacturing system using ant colony optimization algorithm. The International Jour-nal of Advanced Manufacturing Technology.

[11] Na Dasha. 2012. Comparative study of Sino-Russian teacher education course reform. Harbin: Harbin Normal University.

[12] Zhong Xing. 2007. Research of Chinese Language Course under the Perspective of Ecological Course. Guilin: Guangxi Normal University.

Figure

Figure 1. Ant colony foraging schematic.
Table 1. Statistics of calculation results.

References

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