CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals Comparison between simulated annealing algorithms and rapid chain delineation in the construction of genetic maps

2010 ◽  
Vol 33 (2) ◽  
pp. 398-408 ◽  
Author(s):  
Moysés Nascimento ◽  
Cosme Damião Cruz ◽  
Luiz Alexandre Peternelli ◽  
Ana Carolina Mota Campana
Keyword(s):  
Simulated Annealing ◽  
Genetic Maps ◽  
Annealing Algorithms

scholarly journals Quantum annealing algorithms for track pattern recognition

2020 ◽  
Vol 245 ◽  
pp. 10006
Author(s):  
Masahiko Saito ◽  
Paolo Calafiura ◽  
Heather Gray ◽  
Wim Lavrijsen ◽  
Lucy Linder ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Simulated Annealing ◽  
Hadron Collider ◽  
Recognition Algorithm ◽  
Quantum Annealing ◽  
Discovery Potential ◽  
Energy Frontier ◽  
Track Pattern ◽  
Annealing Algorithms ◽  
D Wave

The High-Luminosity Large Hadron Collider (HL-LHC) starts from 2027 to extend the physics discovery potential at the energy frontier. The HL-LHC produces experimental data with a much higher luminosity, requiring a large amount of computing resources mainly due to the complexity of a track pattern recognition algorithm. Quantum annealing might be a solution for an efficient track pattern recognition in the HL-LHC environment. We demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer. The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. The tracking efficiency and purity for the Fujitsu Digital Annealer are nearly the same as the classical simulated annealing.

Sign in / Sign up

Export Citation Format

Share Document