
To solve the problem of traveling salesman (TSP), some intelligent optimization algorithms such as genetic algorithm (GA), ant colony algorithm (ACO), and particle swarm optimization (PSO) algorithm were used. This paper attempts to discuss the optimal solutions and convergence speed change of the three algorithms in solving the problem. We conducted a simulation experiment, found that the PSO provided a better and more stable solution, ACO took the shortest running time and GA is less influenced by the problem scale.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
