scholarly journals Localization of Voltage Regulators in Distribution Systems by a Mixed Genetic–Tabu Search Algorithm

2013 ◽  
Vol 05 (04) ◽  
pp. 751-755 ◽  
Author(s):  
M. C. Pimentel Filho ◽  
M. F. Medeiros
Keyword(s):  
Tabu Search ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Voltage Regulators ◽  
Tabu Search Algorithm

Optimised placement of control and protective devices in electric distribution systems through reactive tabu search algorithm

2008 ◽  
Vol 78 (3) ◽  
pp. 372-381 ◽  
Author(s):  
Luis G. Wesz da Silva ◽  
Rodrigo A. Fernandes Pereira ◽  
Juan Rivier Abbad ◽  
José R. Sanches Mantovani
Keyword(s):  
Tabu Search ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Protective Devices ◽  
Reactive Tabu Search ◽  
Electric Distribution

scholarly journals A Tabu Search Algorithm for Fast Restoration of Large Area Breakdown in Distribution Systems

2010 ◽  
Vol 02 (01) ◽  
pp. 1-5 ◽  
Author(s):  
Jian LIU ◽  
Hongli CHENG ◽  
Xiaojun SHI ◽  
Jingqiu XU
Keyword(s):  
Tabu Search ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Large Area

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

Networks ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 322-340 ◽  
Author(s):  
Richard S. Barr ◽  
Fred Glover ◽  
Toby Huskinson ◽  
Gary Kochenberger
Keyword(s):  
Tabu Search ◽  
Extreme Point ◽  
Search Algorithm ◽  
Fixed Charge ◽  
Tabu Search Algorithm ◽  
Network Problems
Sign in / Sign up

Export Citation Format

Share Document