Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)

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Improved Optimization Algorithm of Ant Colony

Authors
Yunhong Zhao
Corresponding Author
Yunhong Zhao
Available Online May 2016.
DOI
10.2991/icsste-16.2016.98How to use a DOI?
Keywords
Optimization algorithm, convergence, improve operator
Abstract

The mechanisms and basic principles about ant colony algorithm is researched, and in system point of view such characteristics as positive feedback, self-organizing systems, and distributed computing of the ant colony algorithm are analyzed. Analysis, verify and classify improved optimization algorithm of ant colony in detail by TSP-Ei151 in MATLAB 7.6; It shows this algorithm superior to AS in convergence, global and number of iterations.

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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Volume Title
Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
May 2016
ISBN
978-94-6252-177-3
ISSN
2352-5398
DOI
10.2991/icsste-16.2016.98How to use a DOI?
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/).

Cite this article

risenwbib
TY  - CONF
AU  - Yunhong Zhao
PY  - 2016/05
DA  - 2016/05
TI  - Improved Optimization Algorithm of Ant Colony
BT  - Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)
PB  - Atlantis Press
SP  - 528
EP  - 532
SN  - 2352-5398
UR  - https://doi.org/10.2991/icsste-16.2016.98
DO  - 10.2991/icsste-16.2016.98
ID  - Zhao2016/05
ER  -