𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A hybrid method for learning Bayesian networks based on ant colony optimization

✍ Scribed by Junzhong Ji; Renbing Hu; Hongxun Zhang; Chunnian Liu

Publisher
Elsevier Science
Year
2011
Tongue
English
Weight
430 KB
Volume
11
Category
Article
ISSN
1568-4946

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

An ant colony optimization routing based
An ant colony optimization routing based on robustness for ad hoc networks with GPSs
✍ Daisuke Kadono; Tomoko Izumi; Fukuhito Ooshita; Hirotsugu Kakugawa; Toshimitsu M πŸ“‚ Article πŸ“… 2010 πŸ› Elsevier Science 🌐 English βš– 889 KB

a b s t r a c t Ant colony optimization (ACO) routing algorithm is one of adaptive and efficient routing algorithms for mobile ad hoc networks (MANETs). In ACO routing algorithms, ant-like agents traverse the network to search a path from a source to a destination, and lay down pheromone on the path

HOPNET: A hybrid ant colony optimization
HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network
✍ Jianping Wang; Eseosa Osagie; Parimala Thulasiraman; Ruppa K. Thulasiram πŸ“‚ Article πŸ“… 2009 πŸ› Elsevier Science 🌐 English βš– 704 KB

Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) suc

A simulated annealing-based method for l
A simulated annealing-based method for learning Bayesian networks from statistical data
✍ Martin JanΕΎura; Jan Nielsen πŸ“‚ Article πŸ“… 2006 πŸ› John Wiley and Sons 🌐 English βš– 145 KB

The problem of learning Bayesian networks from statistical data is described and reformulated as a discrete optimization problem. For a solution we employ the stochastic algorithm that is known as simulated annealing and that is based on the Markov Chain Monte Carlo approach. Numerical examples are