Article Contents
Article Contents

# A solution of TSP based on the ant colony algorithm improved by particle swarm optimization

• * Corresponding author: Miao Yu.

The first author is supported by Training and Supporting Project for Young or Middle-aged Teachers of China University of Political Science and Law, College Scientific Research Project of China University of Political Science and Law (Grant No. 17ZFG63001) and NSF of China (Grant No. L1422009)

• TSP is a classic problem in the field of logistics, and ant colony algorithm is an important way to solve the problem. However, the ant colony algorithm has some shortcomings in practical application. In this paper, the ant colony algorithm is improved by particle swarm optimization algorithm, and the ant colony algorithm is obtained by giving the ant colony a certain ''particle property''. Finally, an example is given to demonstrate the effectiveness of the improved ant colony algorithm.

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

 Citation:

• Figure 1.  The flow chart of improved ant colony algorithm

Figure 2.  The simulation results of the basic ant colony algorithm

Figure 3.  The simulation results of the improved ant colony algorithm

Table 1.  Observations' latitude and longitude

 Number City Longitude latitude 1 Zhengzhou 113.63E 34.75N 2 Anyang 114.4E 36.1N 3 Hebi 114.3E 35.75N 4 Jiaozuo 113.25E 35.22N 5 Kaifeng 114.32E 34.8N 6 Luohe 114.02E 33.59N 7 Luoyang 112.46E 34.63N 8 Nanyang 112.54E 33N 9 Pingdingshan 113.2E 33.77N 10 Puyang 115.04E 35.77N 11 Sanmenxia 111.21E 34.78N 12 Shangqiu 115.66E 34.42N 13 Xinxiang 113.93E 35.31N 14 Xinyang 114.1E 32.15N 15 Xuchang 113.86E 34.04N 16 Zhoukou 114.7E 33.63N 17 Zhumadian 113.03E 33.02N
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