A Novel Ant Colony Algorithm with Mutation Operations of Genetic Algorithm for TSP

DOI

10.2991/isrme-15.2015.159How to use a DOI?

Keywords

Ant colony algorithm; Mutation operations; Genetic algorithm; TSP

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/isrme-15.2015.159
ID  - Gong2015/04
ER  -