An ant colony optimization and bayesian network structure application for the asymmetric traveling salesman problem

Authors:
Nai-Hua Chen
Affiliations:
Department of Information Management, Chienkuo Technology Univeristy, Changhua City, Taiwan
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Year:
2012

Citing 4
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks

Introduction to Bayesian Networks
Ant Colony Optimization

Ant Colony Optimization
Invited review: A comparative analysis of several asymmetric traveling salesman problem formulations

Computers and Operations Research

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Abstract

The asymmetric traveling salesman problem (ATSP) is an NP-hard problem. The Bayesian network structure which describes conditional independence among subsets of variables is useful in reasoning uncertainty. The ATSP is formed as the Bayesian network structure and solved by the ant colony optimization (ACO) in this study. The proposed algorithm is tested in different sample size. The exam case is finding customer preference's city sequence. Results show the proposed algorithm has a higher joint probability than random selected case. More applications such as the sequential decision, the variable ordering or the route planning can also implement.