11th Joint International Conference on Information Sciences

Convergence Analysis for Generalized Ant Colony Optimization Algorithm

Authors
Daiyuan Zhang 0
Corresponding Author
Daiyuan Zhang
0Nanjing University of Posts and Telecommunications
Available Online December 2008.
DOI
https://doi.org/10.2991/jcis.2008.97How to use a DOI?
Keywords
artificial intelligence, ant colony optimization, ant algorithms, convergence proof, approximation algorithms, GACO algorithm.
Abstract
A new algorithm is proposed, which is called Generalized Ant Colony Optimization (GACO) algorithm. Two new functions are presented to model the behavior for describing the pheromone evaporation and pheromone added to the edges that belong to the best-so-far solution. A class of strictly increasing function is proposed, which gives a general form of expression for the probability of selecting the next node. An important theorem is proved for describing the convergence of GACO algorithm, i.e. for a sufficiently large number of algorithm iterations, the probability of finding the globally optimal solution at least once tends to 1.
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Proceedings
11th Joint International Conference on Information Sciences
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.97How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Daiyuan Zhang
PY  - 2008/12
DA  - 2008/12
TI  - Convergence Analysis for Generalized Ant Colony Optimization Algorithm
BT  - 11th Joint International Conference on Information Sciences
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.97
DO  - https://doi.org/10.2991/jcis.2008.97
ID  - Zhang2008/12
ER  -