A Novel Ant Colony Algorithm with Mutation Operations of Genetic Algorithm for TSP
- https://doi.org/10.2991/isrme-15.2015.159How to use a DOI?
- Ant colony algorithm; Mutation operations; Genetic algorithm; TSP
Ant colony algorithm (ACA) is a very effective way for traveling salesman problem (TSP) due to the positive feedback and the distributed parallel computing mechanism. But it has many drawbacks, for example, long search time, precocity and stagnation. In order to speed up convergence rate and ensure the global search ability, a novel ant colony algorithm was proposed. After every round of search, it uses mutation operations of genetic algorithm to optimize tours and quicken the convergence by comparing path length before and after variation. Experiments show that the improved ant colony algorithm can overcome prematurity and obtain the higher solution accuracy than ACA.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Bencan Gong AU - Peng Chen PY - 2015/04 DA - 2015/04 TI - A Novel Ant Colony Algorithm with Mutation Operations of Genetic Algorithm for TSP BT - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering PB - Atlantis Press SP - 784 EP - 787 SN - 1951-6851 UR - https://doi.org/10.2991/isrme-15.2015.159 DO - https://doi.org/10.2991/isrme-15.2015.159 ID - Gong2015/04 ER -