scholarly journals Development of Future Rule Curves for Multipurpose Reservoir Operation Using Conditional Genetic and Tabu Search Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Anongrit Kangrang ◽  
Haris Prasanchum ◽  
Rattana Hormwichian
Keyword(s):  
Tabu Search ◽  
Simulation Model ◽  
Reservoir Operation ◽  
Climate Model ◽  
Search Algorithm ◽  
Environmental Conservation ◽  
Climate Data ◽  
Optimal Rule ◽  
Rule Curves ◽  
Ipcc Sres

Optimal rule curves are necessary guidelines in the reservoir operation that have been used to assess performance of any reservoir to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements. This study applied the conditional genetic algorithm (CGA) and the conditional tabu search algorithm (CTSA) technique to connect with the reservoir simulation model in order to search optimal reservoir rule curves. The Ubolrat Reservoir located in the northeast region of Thailand was an illustrative application including historic monthly inflow, future inflow generated by the SWAT hydrological model using 50-year future climate data from the PRECIS regional climate model in case of B2 emission scenario by IPCC SRES, water demand, hydrologic data, and physical reservoir data. The future and synthetic inflow data of reservoirs were used to simulate reservoir system for evaluating water situation. The situations of water shortage and excess water were shown in terms of frequency magnitude and duration. The results have shown that the optimal rule curves from CGA and CTSA connected with the simulation model can mitigate drought and flood situations than the existing rule curves. The optimal future rule curves were more suitable for future situations than the other rule curves.

Optimization and risk analyses for rule curves of reservoir operation: application to Tien-Hua-Hu Reservoir in Taiwan

2006 ◽  
Vol 53 (10) ◽  
pp. 317-325 ◽  
Author(s):  
J.-T. Kuo ◽  
N.-S. Hsu ◽  
S.-K. Chiu
Keyword(s):  
Simulation Model ◽  
Reservoir Operation ◽  
Water Shortage ◽  
High Tech ◽  
Domestic Water ◽  
Optimal Rule ◽  
Rule Curves ◽  
Water Demands ◽  
Industrial Parks ◽  
Central Taiwan

Tien-Hua-Hu Reservoir is currently under planning by the Water Resources Agency, Taiwan to meet the increasing water demands of central Taiwan arising from rapid growth of domestic water supply, and high-tech industrial parks. This study develops a simulation model for the ten-day period reservoir operation to calculate the ten-day water shortage index under varying rule curves. A genetic algorithm is coupled to the simulation model to find the optimal rule curves using the minimum ten-day water shortage index as an objective function. This study generates many sets of synthetic streamflows for risk, reliability, resiliency, and vulnerability analyses of reservoir operation. ARMA and disaggregation models are developed and applied to the synthetic streamflow generation. The optimal rule curves obtained from this study perform better in the ten-day shortage index when compared to the originally designed rule curves from a previous study. The optimal rule curves are also superior to the originally designed rule curves in terms of vulnerability. However, in terms of reliability and resiliency, the optimal rule curves are inferior to the those originally designed. Results from this study have provided in general a set of improved rule curves for operation of the Tien-Hua-Hu Reservoir. Furthermore, results from reliability, resiliency and vulnerability analyses offer much useful information for decision making in reservoir operation.

A New Functional Approach for Searching Optimal Reservoir Rule Curves

2014 ◽  
Vol 915-916 ◽  
pp. 1452-1455 ◽  
Author(s):  
Yi Fan Ding ◽  
De Shan Tang ◽  
Zhen Zhu Meng
Keyword(s):  
Reservoir Operation ◽  
Optimization Technique ◽  
Water Shortage ◽  
Functional Approach ◽  
New Approach ◽  
Term Operation ◽  
Optimal Rule ◽  
Rule Curves ◽  
Reservoir Rule Curves

Rule curves are guidelines for long term reservoir operation. An efficient optimization technique is required to find the optimal rule curves that can mitigate water shortage in long-term operation. A new functional approach was proposed to search the optimal rule curves of reservoir. The results indicated that the situations of water shortage and excess release water of using the new approach are smaller than the situations of using the existing rule curves.

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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