Next Article in Journal
Vibration Suppression of a Flexible-Joint Robot Based on Parameter Identification and Fuzzy PID Control
Previous Article in Journal
Special Issue on Reconfiguration Problems
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(11), 188; https://doi.org/10.3390/a11110188

Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

1,2,3,* , 1,2,3
,
1,2,3
and
1,2,3
1
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
3
Beijing Laboratory for Urban Mass Transit, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Received: 17 October 2018 / Revised: 8 November 2018 / Accepted: 16 November 2018 / Published: 19 November 2018
Full-Text   |   PDF [2338 KB, uploaded 19 November 2018]   |  

Abstract

Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy. View Full-Text
Keywords: bayesian network; ant colony optimization; structure learning; cooperative evolution; differential evolution bayesian network; ant colony optimization; structure learning; cooperative evolution; differential evolution
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Xue, Y.; Lu, X.; Jia, S. Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning. Algorithms 2018, 11, 188.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top