Convergence Analysis for Generalized Ant Colony Optimization Algorithm

DOI

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.

Copyright

© 2008, 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  - Daiyuan Zhang
PY  - 2008/12
DA  - 2008/12
TI  - Convergence Analysis for Generalized Ant Colony Optimization Algorithm
BT  - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
PB  - Atlantis Press
SP  - 578
EP  - 583
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.97
DO  - 10.2991/jcis.2008.97
ID  - Zhang2008/12
ER  -