Research on a novel ant colony optimization algorithm

Authors:
Gang Yi;Ming Jin;Zhi Zhou
Affiliations:
Software School, Hunan University, Changsha, Hunan, China;Software School, Hunan University, Changsha, Hunan, China;Department of Computer, Hunan University of Chinese Medicine, Changsha, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

Citing 5
Cited 0

A Graph-based Ant system and its convergence

Future Generation Computer Systems
A proof of convergence for Ant algorithms

Information Sciences—Informatics and Computer Science: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms

IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an adaptive optimization system is established In order to improve the global ability of basic ant colony algorithm, a novel ant colony algorithm which is based on adaptively adjusting pheromone decay parameter has been proposed, and it has been proved that for a sufficiently large number of iterations, the probability of finding the global best solution tends to 1 The simulations for TSP problem show that the improved ant colony algorithm can find better routes than basic ant colony algorithm.