Convergence Analysis for Generalized Ant Colony Optimization Algorithm
Daiyuan Zhang 0
0Nanjing University of Posts and Telecommunications
Available Online December 2008.
- https://doi.org/10.2991/jcis.2008.97How to use a DOI?
- artificial intelligence, ant colony optimization, ant algorithms, convergence proof, approximation algorithms, GACO algorithm.
- 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.
- 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 -