A Swarm Random Walk Algorithm for Global Continuous Optimization

2014 ◽  
pp. 33-43 ◽  
Author(s):  
Najwa Altwaijry ◽  
Mohamed El Bachir Menai
Keyword(s):  
Random Walk ◽  
Continuous Optimization ◽  
Random Walk Algorithm ◽  
Global Continuous Optimization ◽  
Walk Algorithm

Brain perfusion heterogeneity measurement based on Random Walk algorithm: Choice and influence of inner parameters

2010 ◽  
Vol 34 (4) ◽  
pp. 289-297
Author(s):  
Romain Modzelewski ◽  
Elise Janvresse ◽  
Thierry de la Rue ◽  
Pierre Vera
Keyword(s):  
Random Walk ◽  
Brain Perfusion ◽  
Random Walk Algorithm ◽  
Walk Algorithm

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

2014 ◽  
Vol 38 (8) ◽  
pp. 753-763 ◽  
Author(s):  
D.P. Onoma ◽  
S. Ruan ◽  
S. Thureau ◽  
L. Nkhali ◽  
R. Modzelewski ◽  
...  
Keyword(s):  
Random Walk ◽  
Fdg Pet ◽  
Random Walk Algorithm ◽  
Locally Adaptive ◽  
Walk Algorithm

AN IMPROVED RANDOM WALK SEGMENTATION ON THE LUNG NODULES

2013 ◽  
Vol 06 (06) ◽  
pp. 1350043 ◽  
Author(s):  
LI GUO ◽  
YUNTING ZHANG ◽  
ZEWEI ZHANG ◽  
DONGYUE LI ◽  
YING LI
Keyword(s):  
Random Walk ◽  
Lung Nodule ◽  
Texture Feature ◽  
Ellipse Fitting ◽  
Lung Nodule Detection ◽  
Random Walk Algorithm ◽  
Fitting Algorithm ◽  
Nodule Detection ◽  
Walk Algorithm ◽  
Automatic Technique

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

Stochastic Learning for SAT- Encoded Graph Coloring Problems

2010 ◽  
Vol 1 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Noureddine Bouhmala ◽  
Ole-Christoffer Granmo
Keyword(s):  
Random Walk ◽  
Graph Coloring ◽  
Learning Automata ◽  
Frequency Assignment ◽  
Random Walk Algorithm ◽  
Stochastic Learning ◽  
Graph Coloring Problem ◽  
Sat Encoding ◽  
Test Sets ◽  
Walk Algorithm

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

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