article · 2008
Improved Ant Colony Optimization for the Traveling Salesman Problem
Lijie Li, Shangyou Ju, Ying Zhang
Cited by 34Abstract and details only
Abstract
The traveling salesman problem (TSP) in operations research is a classical problem in discrete or combinatorial optimization. It is a prominent illustration of a class of problems in computational complexity theory which are classified as NP-hard. Ant colony optimization inspired by co-operative food retrieval have been widely applied unexpectedly successful in the combinatorial optimization. This paper presents an improved ant colony optimization algorithm for traveling salesman problem, which adopts a new probability selection mechanism by using Held-Karp lower bound to determine the trade-off between the influence of the heuristic information and the pheromone trail. The experiments showed that it can stably generate better solution for the traveling salesman problem than rank-based ant system and max-min ant colony optimization algorithm.
Cite this paper
Li, L., Ju, S., & Zhang, Y. (2008). Improved Ant Colony Optimization for the Traveling Salesman Problem. https://doi.org/10.1109/icicta.2008.265
Li, Lijie, et al. “Improved Ant Colony Optimization for the Traveling Salesman Problem.” 2008, pp. 76–80, https://doi.org/10.1109/icicta.2008.265.
Li, Lijie, Shangyou Ju, and Ying Zhang. 2008. “Improved Ant Colony Optimization for the Traveling Salesman Problem.” https://doi.org/10.1109/icicta.2008.265.
Li, L., Ju, S. and Zhang, Y. (2008) ‘Improved Ant Colony Optimization for the Traveling Salesman Problem’. Available at: https://doi.org/10.1109/icicta.2008.265.
@article{li2008improved,
title = {{Improved Ant Colony Optimization for the Traveling Salesman Problem}},
author = {Li, Lijie and Ju, Shangyou and Zhang, Ying},
pages = {76--80},
year = {2008},
doi = {10.1109/icicta.2008.265}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.