A Cooperative Multilevel Tabu Search Algorithm for the Covering Design Problem

2006 ◽  
pp. 119-130 ◽  
Author(s):  
Chaoying Dai ◽  
(Ben) Pak Ching Li ◽  
Michel Toulouse
Keyword(s):  
Tabu Search ◽  
Design Problem ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Covering Design

A tabu search algorithm for the covering design problem

2010 ◽  
Vol 17 (6) ◽  
pp. 659-674 ◽  
Author(s):  
Kamal Fadlaoui ◽  
Philippe Galinier
Keyword(s):  
Tabu Search ◽  
Design Problem ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Covering Design

Tabu Search Algorithm for Survivable Network Design Problem with Simultaneous Unicast and Anycast Flows

2010 ◽  
Vol 56 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Jakub Gładysz ◽  
Krzysztof Walkowiak
Keyword(s):  
Tabu Search ◽  
Network Design ◽  
Design Problem ◽  
Search Algorithm ◽  
Branch And Cut ◽  
Network Design Problem ◽  
Tabu Search Algorithm ◽  
Survivable Network Design ◽  
Network Cost ◽  
Survivable Network

Tabu Search Algorithm for Survivable Network Design Problem with Simultaneous Unicast and Anycast FlowsIn this work we focus on the problem of survivable network design for simultaneous unicast and anycast flows. This problem follows from the growing popularity of network services applying the anycast paradigm. The anycasting is defined as one-to-one-of-many transmission and is applied in Domain Name Service (DNS), peer-to-peer (P2P) systems, Content Delivery Networks (CDN). In this work we formulate two models that enables joint optimization of network capacity, working and backup connections for both unicast and anycast flows. The goal is to minimize the network cost required to protect the network against failures using the single backup path approach. In the first model we consider modular link cost, in the second we are given a set of link proposal and we must select only one of them. Because these problems are NP-hard, therefore optimal solutions of branch-and-bounds or branch-and-cut methods can be generated for relatively small networks. Consequently, we propose a new heuristic algorithm based on Tabu Search method. We present results showing the effectiveness the proposed heuristic compared against optimal results. Moreover, we report results showing that the use of anycast paradigm can reduce the network cost.

Tabu Search Algorithm for Two-Layer Networks Design Problem

2008 ◽  
Author(s):  
Krzysztof Lenarski ◽  
Bartosz Czajka ◽  
Iwona Pozniak-Koszalka ◽  
Andrzej Kasprzak
Keyword(s):  
Tabu Search ◽  
Design Problem ◽  
Search Algorithm ◽  
Tabu Search Algorithm

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
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