Research of Improved Ant Colony Hybrid Algorithm

DOI

10.2991/mmebc-16.2016.125How to use a DOI?

Keywords

ant colony algorithm, immune algorithm, artificial fish swarm algorithm, hybrid algorithm.

Abstract

In order to extend the application of ant colony algorithm (ACA), many scholars combined the ant colony algorithm with immune algorithm (IA) or other algorithms to solve the problem of slow convergence. To fully solve the too long search time, easily falling into local optimization, slow convergence and some other defects, the immune algorithm and artificial fish swarm algorithm (AFSA) combine with the ant colony algorithm, and the ant colony hybrid algorithm is proposed. Then by solving the traveling salesman problem (TSP), the new algorithm is simulated, and the results show that improving algorithm is effective and feasible.

Copyright

© 2016, 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  - Shijun Li
AU  - Yu Han
AU  - Hongjun Gu
AU  - He Gong
AU  - Jian Li
PY  - 2016/06
DA  - 2016/06
TI  - Research of Improved Ant Colony Hybrid Algorithm
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 582
EP  - 586
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.125
DO  - 10.2991/mmebc-16.2016.125
ID  - Li2016/06
ER  -