Determination of wheat moisture using terahertz spectroscopy combined with tabu search algorithm

2021 ◽  
Author(s):  
Yin Shen ◽  
Chunjiang Zhao ◽  
Bin Li ◽  
Guanglin Li ◽  
Yanxin Yin ◽  
...  
Keyword(s):  
Tabu Search ◽  
Moisture Content ◽  
Terahertz Spectroscopy ◽  
Search Algorithm ◽  
Grain Storage ◽  
Tabu Search Algorithm ◽  
Wheat Grains

The detection of wheat moisture content plays a key role before grain storage and classification. The harvested wheat grains were taken as samples in the current research. A total of...

scholarly journals Penentuan Rute Terpendek Pendistribusian Tabung Gas Lpg 3 Kg Pt. Fega Gas Palu Pratama Menggunakan Algoritma Tabu Search

2020 ◽  
Vol 16 (2) ◽  
pp. 221-229
Author(s):  
Riswan ◽  
A Sahari ◽  
D Lusiyanti
Keyword(s):  
Tabu Search ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Termination Criteria ◽  
Shortest Route ◽  
Tabu Search Method ◽  
Optimum Solution ◽  
Gas Cylinders

ABSTRACTDistribution is one of the important tools in the company business activities. The problem that is often occurred indistribution is the determination of the shortest route. The purpose of this study is optimitation distribution route of3 kg LPG gas cylinders which is carried out by PT. Fega Gas Palu Pratama in Palu City, considering that thiscompany has not used a particular method in determining the distribution route of 3 kg LPG gas cylinders. Themethod used in this study is the Tabu Search algorithm. The algorithm of the Tabu Search method are of follows,first by determine the initial solution using the closest Nearest Neighbor, determine alternatives by exchange 2points in the solution, evaluate alternative solutions, determine a new optimum solution, update the Tabu List, thenwhen the termination criteria are obtained then the Tabu Search algorithm will stop otherwise it will revert toexchanging 2 points evaluation. The process of calculating the Tabu Search algorithm is conducted manually andbuilt using MATLAB. Based on the research that has been done, it is obtained that the shortest, more efficient routeis 21.91 km which has reduction of 7.26 km from the initial route 29.17 km.Keywords : Algorithm Tabu Search, Distribution, Shortest Route.

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

2020 ◽  
Vol 4 (5) ◽  
pp. 884-891
Author(s):  
Salwa Salsabila Mansur ◽  
Sri Widowati ◽  
Mahmud Imrona
Keyword(s):  
Tabu Search ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Search Algorithm ◽  
Urban Transportation ◽  
The Public ◽  
Comparison Results ◽  
Congestion Problems ◽  
Transportation Routes

Traffic congestion problems generally caused by the increasing use of private vehicles and public transportations. In order to overcome the situation, the optimization of public transportation’s route is required particularly the urban transportation. In this research, the performance analysis of Firefly and Tabu Search algorithm is conducted to optimize eleven public transportation’s routes in Bandung. This optimization aims to increase the dispersion of public transportation’s route by expanding the scope of route that are crossed by public transportation so that it can reach the entire Bandung city and increase the driver’s income by providing the passengers easier access to public transportations in order to get to their destinations. The optimal route is represented by the route with most roads and highest number of incomes. In this research, the comparison results between the reference route and the public transportation’s optimized route increasing the dispersion of public transportation’s route to 60,58% and increasing the driver’s income to 20,03%.

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

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

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