scholarly journals Optimal Network Destruction Strategy with Heterogeneous Cost under Cascading Failure Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fang Yang ◽  
Tao Ma ◽  
Tao Wu ◽  
Hong Shan ◽  
Chunsheng Liu
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Network Performance ◽  
Search Algorithm ◽  
Cascading Failure ◽  
Tabu Search Algorithm ◽  
Failure Model ◽  
The Cost ◽  
Node Removal ◽  
The Impact

By studying an attacker’s strategy, defenders can better understand their own weaknesses and prepare a response to potential threats in advance. Recent studies on complex networks using the cascading failure model have revealed that removing critical nodes in the network will seriously threaten network security due to the cascading effect. The conventional strategy is to maximize the declining network performance by removing as few nodes as possible, but this ignores the difference in node removal costs and the impact of the removal order on network performance. Having considered all factors, including the cost heterogeneity and removal order of nodes, this paper proposes a destruction strategy that maximizes the declining network performance under a constraint based on the removal costs. First, we propose a heterogeneous cost model to describe the removal cost of each node. A hybrid directed simulated annealing and tabu search algorithm is then devised to determine the optimal sequence of nodes for removal. To speed up the search efficiency of the simulated annealing algorithm, this paper proposes an innovative directed disturbance strategy based on the average cost. After each annealing iteration, the tabu search algorithm is used to adjust the order of node removal. Finally, the effectiveness and convergence of the proposed algorithm are evaluated through extensive experiments on simulated and real networks. As the cost heterogeneity increases, we find that the impact of low-cost nodes on network security becomes larger.

scholarly journals Human Resource Scheduling Model and Algorithm with Time Windows and Multi-Skill Constraints

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 598 ◽  
Author(s):  
Zhiping Zuo ◽  
Yanhui Li ◽  
Jing Fu ◽  
Jianlin Wu
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Search Algorithm ◽  
Time Windows ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Service Personnel ◽  
The Stability

In situations where an organization has limited human resources and a lack of multi-skilled employees, organizations pay more and more attention to cost control and personnel arrangements. Based on the consideration of the service personnel scheduling as well as the routing arrangement, service personnel of different skills were divided into different types according to their multiple skills. A mathematical programming model was developed to reduce the actual cost of organization. Then, a hybrid meta heuristic that combines a tabu search algorithm with a simulated annealing was designed to solve the problem. This meta heuristic employs several neighborhood search operators and integrates the advantages of both the tabu search algorithm and the simulated annealing algorithm. Finally, the stability and validity of the algorithm were validated by the tests of several kinds of examples.

Cost-Effective Tabu Search Algorithm for Solving the Controller Placement Problem in SDN

2020 ◽  
pp. 109-130
Author(s):  
Richard Isaac Abuabara ◽  
Felipe Díaz-Sánchez ◽  
Juliana Arevalo Herrera ◽  
Isabel Amigo
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Cost Effective ◽  
Security Requirements ◽  
Tabu Search Algorithm ◽  
Placement Problem ◽  
Controller Placement ◽  
Network Operation ◽  
Controller Placement Problem ◽  
The Cost

Software-defined networks (SDN) is an emerging paradigm that has been widely explored by the research community. At the same time, it has attracted a lot of attention from the industry. SDN breaks the integration between control and data plane and creates the concept of a network operating system (controller). The controller should be logically centralized, but it must comply with availability, reliability, and security requirements, which implies that it should be physically distributed in the network. In this context, two questions arise: How many controllers should be included? and Where should they be located? These questions comprise the controller placement problem (CPP). The scope of this study is to solve the CPP using the meta-heuristic Tabu search algorithm to optimize the cost of network operation, considering flow setup latency and inter-controller latency constraints. The network model presented considers both controllers and links as IT resources as a service, which allows focusing on operational cost.

scholarly journals DESIGNING ROUTE DISTRIBUTION USING TWO PHASE TABU SEARCH ON HETEROGENOUS FLEET VEHICLE ROUTING PROBLEM WITH TIME WINDOW IN PT. XYZ TO MINIMIZE TRAVEL DISTANCE

2019 ◽  
Vol 3 (01) ◽  
pp. 6-14
Author(s):  
Citra Septi Brilliane ◽  
Ari Yanuar Ridwan ◽  
Rio Aurachman
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Search Algorithm ◽  
Travel Distance ◽  
Tabu Search Algorithm ◽  
Two Phase ◽  
Routing Problem ◽  
The Impact

XYZ is a pasteurization milk processing company that produce milk drink from pure cow milk. PT. XYZ don’t sell their product directly to end user, instead they distribute their product to many companies which serve milk for their employees or operators in lunch time. So, their customer is mostly a manufacture company from various kinds of industry. They have about 40 customers and most of them are outside Bandung. However, the delivery may not be done as planned. The average on time delivery is around 96%. it is below PT. XYZ target which is 98%. The impact of the delay itself is vary between customers. Because when delay occur, each customer has their own regulation that has been settled in agreement contract. Based on the delay recapitulation above, there are several factors that caused this problem. Delay in departure is the most influential factors. It is because PT. XYZ don’t have fixed schedule of delivery and they miscalculate the departure time because of improper route determination that also leads to longer travel time. This case is categorized as Vehicle Routing Problem with Heterogeneous Fleet and Time Window (VRPHFTW) that will be solved using one of meta-heuristics algorithm which is Two Phase Tabu Search Algorithm to minimize travel distance. In the end, the travel distance can be minimized 19.48%. Keywords—Vehicle Routing Problem with Heterogenous Fleet and Time Window (VRPHFTW), Meta-Heuristics, Two Phase 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.

Sign in / Sign up

Export Citation Format

Share Document