Data Research, Vol. 2, Issue 1, Feb  2018, Pages 18-32; DOI: 10.31058/j.data.2018.21002 10.31058/j.data.2018.21002

Research on the Internet of Things Based on Ant Colony Optimization Algorithm

, Vol. 2, Issue 1, Feb  2018, Pages 18-32.

DOI: 10.31058/j.data.2018.21002

Yibin Hou 1* , Jin Wang 1

1 School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China

Received: 1 December 2017; Accepted: 25 December 2017; Published: 8 February 2018

Abstract

The purpose of this paper is to prove that the ant colony algorithm is an excellent mathematical modeling method and improve the production efficiency of the single drilling machine. The methods of mathematical induction and mathematical deduction and mathematical hypothesis are commonly used mathematical methods in scientific research. The ant colony algorithm to solve the TSP problem: algorithm design ideas: using the standard ant colony algorithm or its improved to achieve a traveling salesman problem TSP, find the shortest distance of the 51 City, the number of iterations is 1000 times, the final output of the optimal solution. Algorithm flow: (1) initialize ant colony: initialize ant colony parameter, set ant number, ant put in 51 vertices, initialize path pheromone. (2) Ant action: the ants leave their paths by the ants in front of the pheromone and their own judgments to complete a loop path. (3) Releasing pheromones: the path to releasing ants through a certain percentage of pheromones. (4) The evaluation of ants: the fitness is evaluated according to the objective function of each ant. (5) If the shortest path condition is satisfied, the optimal output is obtained. Otherwise, the algorithm continues. (6) Pheromones evaporate: pheromones continue to dissipate over time. The result of this paper is that the ant colony algorithm has high accuracy and efficiency; the TSP problem can be solved, to improve the production efficiency of the single drilling machine. The conclusion of this paper is that ant colony algorithm is an excellent algorithm, the TSP problem can be solved, for example, can improve the production efficiency of the single drilling machine.

Keywords

Internet of Things, Colony Optimization Algorithm, TSP Problem, the Production Efficiency of the Single Drilling Machine

1. Introduction

The Internet of things is a kind of Internet, and all the problems in this article are studied under the Internet of things. The methods of mathematical induction and mathematical deduction and mathematical hypothesis are commonly used mathematical methods in scientific research. Artificial intelligence includes machine learning, and machine learning includes ant colony algorithm [1-10].

In reference [48], ant colony optimization (ACO) is pointed out as a naturally inspired swarm intelligence algorithm, which belongs to the category of meta heuristic algorithm. It is derived from the simulation of ant colony foraging behavior in nature. When ants are foraging, they secrete a chemical hormone pheromone on the path they pass through, and at the same time, judge the direction of progress according to the concentration of pheromone nearby. The individual behavior pattern in the ant colony is relatively simple, and its interaction is only through a single scalar letter. The concentration of the pheromone is carried out indirectly. But the independent and parallel behavior of multiple individuals shows the whole population. Complex, intelligent behavior. The ant colony system is naturally distributed, self-learning, self-organizing, robust, and simple. The single character makes the ant colony optimization algorithm more powerful than the traditional mathematical method in solving the optimization problem. The ability to solve the problem has been widely concerned and applied in all fields of production and life. The key of the ant colony optimization algorithm is a parameterized probability model, called the pheromone model. At each iteration, the system generates population based on pheromone model, and then reflects the generated population information in the pheromone model to affect the next generation population. That is to say, the upper and lower generations of the population communicate indirectly through the pheromone model. The pheromone model is not only a priori knowledge to guide the evolutionary direction of the population, but also a posteriori knowledge of preserving the information of the historical population. Therefore, the selection of the pheromone model directly determines the performance of the whole ant colony optimization algorithm. Reference [49] points out that the theoretical research of ant colony optimization algorithm is helpful to better understand the principle of the algorithm and to guide the application of the algorithm. The convergence analysis, time complexity analysis and approximate performance analysis of the ant colony optimization algorithm are reviewed. The object of theoretical research is transformed from a simple pseudo Boolean function to a combinatorial optimization problem and its practical application. The theoretical research of ant colony algorithm is reviewed from 2 aspects:the theoretical analysis method of ant colony algorithm and the type of research problem. The basic mathematical analysis tools such as adaptive value division and drift analysis are introduced, and some important problems, such as time complexity and approximate performance, are discussed. The performance of an ant colony algorithm for solving all kinds of problems is summarized and compared, and it is pointed out that these studies can be more in-depth understanding of the operating mechanism of the ant colony algorithm. Finally, we discuss the problems to be solved in the theoretical research of the ant colony algorithm. It is pointed out that the introduction of new analytical tools and the research of more complex algorithm models are the directions and contents worthy of further research. Reference [50] points out that with the rapid development of network technology, the demand for multimedia services has increased dramatically. Multicast as a basic technology of point to multipoint, can support multimedia business well, so it has attracted wide attention. The traditional "Store-and-Forward"method is used for data forwarding in traditional sister sowing, which cannot guarantee the maximum multicast rate in theory. In 2000, network coding was proposed. The technology retransmitted data according to the "code forwarding (Coding-and-Forward)"mode, which made up for the shortcomings of traditional technology, and enabled multicast to better support multimedia services with increasing banddelwidth demand. In the early research of network coding and multicast, most of them assume that all nodes with coding function carry out coding operation. However, encoding operation requires additional computation and storage resources, which brings extra computation cost and time delay. Therefore, the problem of network coding resource optimization is proposed, which is to ensure the maximum rate of multicast and reduce the coding operation as much as possible. Ant colony optimization algorithm has been successfully applied to many combinatorial optimization problems, but it has not been applied to this problem. This paper studies the ant colony optimization algorithm to solve the optimization problem of network coding resources. The reference [51] points out that continuous ant colony optimization algorithm is an important research direction of ant colony optimization algorithm, ant colony optimization algorithm for continuous domain (ACOR) longer computing time and easy to fall into the local optimum problem, put forward a kind of continuous ant colony optimization algorithm based on artificial bee colony (ABCACOR). First of all, the introduction of an alternative mechanism to guide the selection of solutions, to replace the original selection method based on sorting, to save calculation time and diversity as much as possible to keep the search;secondly, combined with the artificial bee colony algorithm search strategy to improve the global search capability of the algorithm, further reduce the computation time and improve the accuracy of the solution. Simulation experiments on a large number of test functions show that the ABC ACOR algorithm has better performance than the existing ant colony algorithm in continuous domains. Reference [52] pointed out that the ant colony algorithm itself has slow convergence speed and easy to fall into the local optimal solution, and proposes some improved ant colony optimization algorithm for this defect. Mainly discusses the convergence of ant colony optimization algorithm theory and application, the performance of ant system and the max min ant system is better than the performance of ant system and the max min ant system, ant system and the max min ant system is a special kind of convergence. It is crucial to find the problems to be solved according to the current research situation at home and abroad. The research status at home and abroad has been written a lot and a lot of work has been done. Research basis is the basis of research, literature research, practical research, experiments, and so on. In the process of finding problems, the research foundation, the research basis and the research status at home and abroad have been written a lot. There are two cases of propositional paper and non propositional paper. Is there a difference between the application value and the specific application? The data should be obtained in the first part of the experiment, the regression of the law and the revaluation of the law, then the experiment, and the regression of the rules and then the revaluation. The price of a sensor in the Zhichun Road electronic city is about 90-200 yuan. The method of mathematical modeling and the method of writing code are an important research method.

1.1. The Ant Colony Algorithm to Solve TSP Problem

The ant colony algorithm to solve the TSP problem:algorithm design ideas:using the standard ant colony algorithm or its improved to achieve a traveling salesman problem TSP, find the shortest distance of the 51 City, the number of iterations is 1000 times, the final output of the optimal solution. Algorithm flow:(1) initialize ant colony:initialize ant colony parameter, set ant number, ant put in 51 vertices, initialize path pheromone. (2) Ant action:the ants leave their paths by the ants in front of the pheromone and their own judgments to complete a loop path. (3) Releasing pheromones:the path to releasing ants through a certain percentage of pheromones. (4) The evaluation of ants:the fitness is evaluated according to the objective function of each ant. (5) If the shortest path condition is satisfied, the optimal output is obtained. Otherwise, the algorithm continues. (6) Pheromones evaporate:pheromones continue to dissipate over time. Basic design idea:(L) pre initialization of pheromone intensity and ant tabu list. Ants follow the rules in the tabu list of certain probabilities and arrive at the nodes at the next choice until a legitimate path is formed. (2) Calculatethe length of the path generated by each ant, and the path length is the sum of the length of each path. (3) Update the pheromone on each side. Each side of the first pheromone volatile operation, and then, according to the path length generated by ants, to get the release of acne by pheromones. (4) when all the ants have completed the pheromone update operation, record the current shortest paths, and the tabu list and the pheromone value added in the △( ttl) is initialized, and go to step 2. And so on, until the end of the algorithm is satisficannoth as the solution can not be further improved or reached the predetermined number of cycles. First, set up an ant class, and then set up a TSP class, call the ants in TSP class variables and methods, and then define the main function, in the main function calls the TSP class variables and methods [1-10].

1.2. The Artificial Ant Colony Algorithm for TSP Problem

In the artificial ant colony algorithm of TSP problem, it is assumed that the M, ant moves between the adjacent nodes of the graph, thus the solution of the problem can be obtained asynchronously and asynchronously. The probability of a step shift for each ant is determined by the two parameter on each edge of the graph:(1) Pheromone values are also called pheromone traces. (2) Visibility, that is, a priori value. There are 2 kinds of information update in a way, is volatile, which is all the information on the path for a certain ratio of reduction, process simulation of natural pheromone volatile over time;the two are to enhance the value, to "good"(ants through side) increase pheromone. The downward movement of a target is realized by a random principle, is the use of the storage node information, the next step is to calculate the probability of a node, and according to the probability of achieving a step by the reciprocating movement, more and more close to the optimal solution. Ants in the search process, or find a solution, will evaluate the optimization of the solution or part of the solution, and the evaluation information stored in the relevant connection pheromone. Ant colony algorithms are used to find the shortest distance between 51 cities [11-20]. The flowchart of the ant colony algorithm is shown in Figure 1 .

Figure 1. Flow chart of ant colony algorithm.

1.3. The Idea of Algorithm Design

Using the standard ant colony algorithm or its improved traveling salesman problem TSP, find the shortest distance between 51 cities, the number of iterations 1000 times, and finally output the optimal solution. The basic flow of the algorithm is:in the artificial ant colony algorithm for the TSP problem, it is assumed that the M, ant moves between the adjacent nodes of the graph, thus the solution of the problem can be obtained asynchronously and asynchronously. One step of each ant's transition probability is determined by two parameters on each edge of the graph: (1), pheromone values are also called pheromone traces. (2) visibility, that is, a priori value. There are 2 kinds of information update in a way, is volatile, which is all the information on the path for a certain ratio of reduction, process simulation of natural pheromone volatile over time;the two are to enhance the value, to "good"(ants through side) increase pheromone. The downward movement of a target is realized by a random principle, is the use of the storage node information, the next step is to calculate the probability of a node, and according to the probability of achieving a step by the reciprocating movement, more and more close to the optimal solution. Ants in the search process, or find a solution, will evaluate the optimization of the solution or part of the solution, and the evaluation information stored in the relevant connection pheromone [21-30].

1.4. Algorithm Flow

Algorithm flow:(1) Initializing ants:initializing ant colony parameters, setting ants quantity, placing ants on 51 vertices, initializing path pheromone. (2) Ant movement:the ant chooses the path according to the pheromone left by the ant in front and its own judgment, and completes a cycle. (3) Release pheromone:release the pheromone in a certain proportion to the path of the ants. (4) Evaluate the ant colony:evaluate the fitness of each ant, according to the objective function. (5) If the condition of the shortest path is satisfied, the output optimal solution is obtained. Otherwise, the algorithm continues. (6) The pheromone pheromone volatilization:as time continues to dissipate.

1.5. Ant Colony Optimization Algorithm

Ant colony optimization algorithm: (1) A group of ants starts randomly from the starting point, meets food, holds food, and returns along the road. (2) Ants on the way back and forth, leaving pheromone signs on the road. (3) The pheromone will gradually evaporate over time (usually available as a negative exponential function or released at a certain rate). (4) From the nest of ants, the path selection and the probability of each path pheromone concentration is proportional to the. Note:the same principle can be used to describe the foraging situation of multiple food sources by ant colonies. The framework of general ant colony algorithm has three components:A: the activity of ant colony;B:the volatilization of pheromone;C: the enhancement of pheromone;the transition probability formula and the pheromone update formula are mainly reflected in the previous algorithm [31-40].

1.6. Basic Design Idea

Basic design idea:

(t):t times the number of ants in the city of i;

m:The total number of ants in an ant colony, m=;

:Pheromone intensity on edges (i, j);

:Visibility on sides (i, j);

:The distance between city i and city j;

:The probability of the transfer of ant k from urban i to urban j.

Pre initialization pheromone intensity on each side, and tabu list of ants. In accordance with certain rules of probability, the ants select the next node to be reached under the restriction of the tabu list, and eventually form a legitimate path. (2) Pre initialization pheromone intensity on each side, and tabu list of ants. In accordance with certain rules of probability, the ants select the next node to be reached under the restriction of the tabu list, and eventually form a legitimate path. (3) Update the pheromone on each side. Each side first carries out pheromone volatilization operation, and then obtains the pheromone released by ants according to the path length generated by each ant. (4) When all ants have completed the pheromone update operation, the current shortest path is recorded, and the tabu table and pheromone values are added ( ttl) proceed to initialization and go to step (2). And so on, until the end of the algorithm is satisfied, such as the solution cannot be further improved or reached the predetermined number of cycles. First, set up an ant class, and then set up a TSP class, call the ants in TSP class variables and methods, and then define the main function, in the main function calls the TSP class variables and methods.

2. Methods

A hole is a printed circuit board (also known as printed circuit board) one of the important components of the processing cost holes usually accounted for 30% to 40% of the cost of the system board, punching machine is mainly used in the drilling process in the manufacture of printed circuit board, the problem is to improve the production efficiency of a certain kind of punch.

The production efficiency of drilling depends mainly on the following two aspects: (1) the drilling time of the through hole, which is determined by the manufacturing process, assumes that the operation time for each hole is the same.(2) punch in the processing operation, bit travel time (or walk), assuming the drill only horizontal and vertical motion. A company shall produce a batch of printed circuit boards of large quantity and of the same specifications, and the data for the through-hole coordinates are as follows:please design an optimum through-hole plan for the company.

Data figure as shown below in Figure 2 , (x, y) represents the center coordinate data of the hole;the Center coordinates data of printed circuit board as shown in Table 1 .

Figure 2. Data figure.

Table 1. Center coordinates data on printed circuit board.

x y

x y

x y

x y

x y

288 149

32 121

48 73

64 21

260 37

288 129

32 113

56 73

72 25

260 45

270 133

40 113

56 81

80 25

260 53

256 141

56 113

48 83

80 25

260 61

256 157

56 105

56 89

80 41

260 69

246 157

48 99

56 97

88 49

260 77

236 169

40 99

104 97

104 57

276 77

228 169

32 97

104 105

124 69

276 69

228 161

32 89

104 113

124 77

276 61

220 169

24 89

104 121

132 81

276 53

212 169

16 97

104 129

140 65

284 53

204 169

16 109

104 137

132 61

284 61

204 169

16 109

104 137

132 61

284 61

196 169

8 109

104 145

124 61

284 69

188 169

8 97

116 145

124 53

284 77

196 161

8 89

124 145

124 45

284 85

188 145

8 81

132 145

124 37

284 93

172 145

8 73

132 137

124 29

284 101

164 145

8 65

140 137

132 21

288 109

156 145

8 57

148 137

124 21

280 109

148 145

16 57

156 137

120 9

276 101

140 145

8 49

164 137

128 9

276 93

148 169

8 41

172 125

136 9

276 85

164 169

24 45

172 117

148 9

268 97

172 169

32 41

172 109

162 9

260 109

156 169

32 49

172 101

156 25

252 101

140 169

32 57

172 93

172 21

260 93

140 145

8 49

164 137

128 9

276 93

148 169

8 41

172 125

136 9

276 85

164 169

24 45

172 117

148 9

268 97

172 169

32 41

172 109

162 9

260 109

156 169

32 49

172 101

156 25

252 101

140 169

32 57

172 93

172 21

260 93

132 169

32 65

172 85

180 21

260 85

124 169

32 73

180 85

180 29

236 85

116 161

32 81

180 77

172 29

228 85

104 153

40 83

180 69

172 37

228 93

104 161

40 73

180 61

172 45

236 93

104 169

40 63

180 53

180 45

236 101

90 165

40 51

172 53

180 37

228 101

80 157

44 43

172 61

188 41

228 109

64 157

44 35

172 69

196 49

228 117

64 165

44 27

172 77

204 57

228 125

56 169

32 25

164 81

212 65

220 125

56 161

24 25

148 85

220 73

212 117

56 153

16 25

124 85

228 69

204 109

56 145

16 17

124 93

228 77

196 101

56 137

24 17

124 109

236 77

188 93

56 129

32 17

124 125

236 69

180 93

56 121

44 11

124 117

236 61

180 101

40 121

56 9

124 101

228 61

180 109

40 129

56 17

104 89

228 53

180 117

40 137

56 25

104 81

236 53

180 125

40 145

56 33

104 73

236 45

196 145

40 153

56 41

104 65

228 45

204 145

40 161

64 41

104 49

228 37

212 145

40 169

72 41

104 41

236 37

220 145

32 169

72 49

104 33

236 29

228 145

32 161

56 49

104 25

228 29

236 145

32 153

48 51

104 17

228 21

246 141

32 145

56 57

92 9

236 21

252 125

32 137

56 65

80 9

252 21

260 129

32 129

48 63

72 9

260 29

280 133

2.1. Problem Analysis

Problem analysis: This will improve the production efficiency of the machine and make an analysis, the production efficiency refers to the unit of time production capacity, processing efficiency. So in order to improve the production efficiency of drilling, we can make the travel time and the total drill tool change time as short as possible, the production efficiency will be higher.

For a single drill, we first draw the distribution of all points to determine that the diagonal of the circuit is approximately, and that the speed of travel is rough and that the time of travel is not long. Considering the moving speed and the conversion time of the tool, it is found that the conversion time of the tool is much longer than the travel time, so we want to achieve the shortest time when the tool's conversion time is the best. Therefore, we use an ant colony algorithm to compute the shortest distance between the shortest distance and the least number of tool conversions, and compare the two, and get the optimal results.

2.2. Model Hypothesis

Model hypothesis:

(1) The drilling time of a single through the hole is determined by the manufacturing process. In order to simplify the problem, it is assumed that the drilling time for the same pass is the same.

(2) In order to calculate the travel expenses, need to calculate the travel time, in order to simplify the problem, assuming the punch travel is uniform motion.

(3) It is assumed that the change over time of the tool is the same for different pass processing operations;

(4) Between the two hole distance calculation, to simplify the problem, the drill bit is a particle hypothesis punch.

(5) In order to avoid the touch and interference between the drills, it is assumed that the distance between the two drills is not less than 3cm.

2.3. Model Establishment

Model establishment:A single bit model:for a given size of the hole, adjust the corresponding tool, from the point of the tool knife along the shortest distance path, moving from one hole to another hole, the hole until all objects is processed, processing the other hole then the next size conversion tool, such an arrangement. Describe the problem as the following optimization model:

(1) Variable design. A collection with n holes ……set upRepresents any two holes in a collection,Represents the distance between two holes in a collection,M is the total distance traveled.

(2) Objective function. You need to find a non repeating whole arrangement in the hole collection

order , find the minimum value of M.

(3) Constraints:the machining path starts from one hole, processes only one hole at each hole, traverses each hole, and finally goes back to the starting point, including tool switching.

(4) Optimization algorithm:ant colony algorithm.

3. Results

Results analysis of single drill bit:For the single bit mode of production, way of using a drilling tool for drilling holes corresponding to all a tool of the corresponding hole drilling in conversion tool, the line of work by using ant colony algorithm, data processing by MATLAB. (In order of order Cutting tool:defghabcf, Pass:DGDIJFGHFACBCEIJEG.

The following table shows the stroke of each tool (unit: 104mil), the stroke of each tool as shown in Table 2 .

Table 2. The stroke of each tool.

tool

d

e

f

g

h

a

b

c

f

Trip

5.9257

5.9527

4.6661

3.2375

3.0941

12.586

11.573

11.123

4.8309

time/s

8.361821

8.399921

6.584386

4.568472

4.366119

17.76024

16.33079

15.69579

6.816937

According to the above table, under the optimal line condition of single drill operation, the total length of various tool operations can be as follows:62.989e+004 mil. The following is the point of view we have set for each tool (1/100mil), the point of view we have set for each tool as shown in Table 3 .

Table 3. The point of view we have set for each tool.

d

e

f

g

h

a

b

c

f

x

-267400

-257400

-222047

-301300

-311300

-202800

-197600

-202800

199800

y

184518

184518

100000

84300

74300

190200

180800

190200

203200

4. Discussion

The distance from the previous table and the distance traveled for the selection of 1.2471e+004mil, plus the distance of advance, shows that the total distance of the route is Shortest_Length= 64.2361e+ 004 (mil) = 16315.97 (mm). The speed of movement of all bits is the same, and the drilling time of all bits of the drill is

12*18=216 (s). Timing:job travel time = shortest distance / bit travel speed =90.64427 (s),

Total operating time = drill travel time + drill conversion time =306.64 (s),

Cost:travel cost = travel time * travel cost =978.9594 (yuan),

Job conversion cost = conversion times * conversion cost =25.2 (yuan),

Total operating cost = travel cost + job transfer cost =1004.159 (yuan),

That is to say, this method will cost 306.64 (s) and cost 1004.159 (yuan) when needed[43-47].

Acknowledgments

Thanks to the National Natural Science Foundation of China (No:61203377, 60963011, 61162009), the work was supported by scholarships and fellowships for tutors, doctoral scholarships and fellowships from the Software Institute of Beijing University of Technology and the Faculty of information. Here, thank you very much for Hou Yibin tutor support and help and guide the care, thanks to Mentor Professor Hou Yibin zhiyuzhien. Thank you very much for your mentors, providing good research platform and environment, providing opportunities for self - exercise, building and providing self - value platform, providing strong help and support, and teaching research methods. Thank you very much for the teacher's knowledge and careful guidance, provides a good experimental environment for students, Hou teacher plays a key role in my study and research, and the growth of each character every step cannot do without the guidance of the teacher hou. Hou's academic attainments are very high, both theoretical and practical. Thanks to Professor Hou Yibin for my work, learning, research, life, all aspects of care, guidance and help, in his proper guidance, it is easier to write a paper, published many (including ISTP, EI, SCI conferences and journals) and IEEE papers. Hou's domestic and international perspective and keen insight and academic in Beijing and the Beijing University of Technology and social work methods and in-depth understanding of teamwork and selfless work attitude and painstaking work style and painstaking patience to cultivate students advocating morality, benefited me a lifetime. Hou has worked hard for my study and research. This is an inspiring mentor; he is generous in the atmosphere.

Copyright

© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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