- 期刊
Comparative Study on Adjustable Parameter Interface of TSP based on Ant Colony Algorithm and Particle Swarm Algorithm
摘要
With the continuous development of intelligent algorithms, new algorithms emerge one after another, each has its own advantages and disadvantages. At the same time, the performance comparison of different algorithms has become an important work to test the algorithm. In this paper, the problem of travel agent (TSP) is studied by using the visualization and operability of GUI in MATLAB. The simulation of the interface of adjustable parameters is carried out for ant colony algorithm and particle swarm algorithm respectively. Through the running program, it can be concluded that ant colony algorithm has positive feedback of pheromone, particle swarm optimization has fast and global convergence, and the advantages of both can be complementary.
參考文獻
Dorigo M, Gambardella LM. Ant colony system: a cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997.
S Y Sang. Study on the Optimal Travel Route Selection Based on Ant Colony Algorithm Model[J]. IOP Conference Series: Earth and Environmental Science, 2020, 513(1).
Muhammad Imran, Rathiah Hashim and Noor Elaiza Abd Khalid. An Overview of Particle Swarm Optimization Variants[J]. Procedia Engineering, 2013, 53.
P. Guo, W.J. Yan. Survey of ant colony algorithm based on TSP problem [J]. Computer science, 2007 (10), 181-184+194.
M.L. Li, X.Yu, D.Y. Wu, et al. Research on path planning based on ant colony TSP algorithm [C]. China command and control society, 2018, 7.