YEN-CHING CHANG2026-10-162026-10-162017-11-27Pure ID: 15461686Pure UUID: 0f6e4f52-1512-40ef-a29e-384853abb19f10.1109/SMC.2017.8122633https://www.scopus.com/pages/publications/85044414067https://scholars.csmu.edu.tw/handle/123456789/22946Ant colony optimization (ACO) is a quite mature optimization algorithm for combinational problems, but it still attracts many researchers trying to raise its efficiency and/or performance. Some of them endeavor to speed up or improve ACO by choosing more suitable parameters of iteration or update formulas. This work tries to introduce k-means clustering to enhance the efficiency of ACO for the traveling salesman problem. First, the city locations are clustered into two or more groups according to k-means clustering. Second, ACO is separately applied to these groups of cities. Finally, an appropriate connection technique is used to join these groups; every two groups are connected until all groups are connected into a single route. Experimental results show that the computational cost of multiple groups divided by k-means clustering can be reduced to below 32 percent of the time run by the original group, and even multiple groups contribute to raising the performance in a particular distribution of cities.6enjournal article