Tabu Search Algorithm (TSA): A Comprehensive Survey

Prajapati, Vishnu Kumar; Jain, Mayank; Chouhan, Lokesh

doi:10.1109/icetce48199.2020.9091743

Cited by 42 publications

(26 citation statements)

References 15 publications

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“…Tabu search algorithm (TSA) is a very efficient method in the field of combinatorial optimization [41]. As a local-search metaheuristic algorithm, tabu search drives solution space to avoid local optima and cycling which as the weakness of heuristic approaches within a set of neighborhoods of solutions.…”

Section: A Tabu Search Algorithmmentioning

confidence: 99%

An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

Zhou

Liu

et al. 2021

IEEE Access

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To reduce recovery cost of repairing multiple failed nodes, many repair schemes have been proposed for erasure codes based distributed storage systems. However, most of the existing researches ignore the network topology of storage devices. Motivated by such considerations, we combine delay repair schemes with network topology and propose a tree-structured model based on fountain codes with large value of (, ,) to improve the repair efficiency. More precisely, with the consideration of network topology, a new target named data recovery cost is defined to measure the efficiency of coded fragment download and source file reconstruction, and then the optimal recovery threshold is derived to minimize the average data recovery cost of general tree-structured model. Moreover, we analyze and compare the average data recovery cost of general tree-structure with different systematic parameters. To further improve the data transmission efficiency, an optimal tree-structured scheme based on improved tabu search algorithm (ITSA-ORT) is proposed. Compared with other algorithms, the ITSA-ORT scheme uses Prim algorithm to generate the initial solution and then uses special method to obtain the corresponding neighborhood structure. The experimental results show that the proposed scheme can find a globally optimal solution and obtain lower cost of data recovery. In addition, the ITSA-ORT scheme has lower computational complexity than the optimal treestructured scheme based on particle swarm optimization algorithm (PSO-ORT) and the optimal treestructured scheme based on firefly algorithm (FA-ORT). INDEX TERMS Centralized communication model, fountain codes, delay repair, network topology, treestructured model, optimization algorithms.

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Section: A Tabu Search Algorithmmentioning

confidence: 99%

An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

Zhou

Liu

et al. 2021

IEEE Access

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“…The previously proposed methods are broadly classified into two directions: one is to make a selection in a given finite set of candidates, such as LAMA, and the other is to search the optimal solution using the greedy algorithm. In order to optimize the multicast delay, our algorithm uses the idea of the Tabu search (Prajapati et al, 2020) In order to optimize the rerouting strategies suitable for both satellite and terrestrial networks, a two-layered shared tree structure is used to determine RPs and construct shared trees in both satellite and terrestrial networks. Firstly, at the beginning of each time slot, we set the connection status of the whole network links and choose the satellite connected to the terrestrial networks through the longest visual time criterion based on terrestrial macro base stations.…”

Section: Dynamic Shared Tree Buildingmentioning

confidence: 99%

A Two-Layered Shared Tree Multicast Routing Algorithm for Software Defined Hybrid Satellite-Terrestrial Communication Networks

Xiong

Dai

2021

Front. Comms. Net.

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Dynamic routing and congestion control are two major problems in software-defined hybrid satellite-terrestrial multicast networks research. Due to terrestrial users being allowed to join or leave the multicast group at any time and the differences between the satellite and the terrestrial networks, many multicast routing algorithms reroute rapidly and thus increase the rerouting overheads. Meanwhile, the congestion ratio is increased by some hot nodes of satellite-terrestrial link transmission paths. This paper focuses on rerouting overheads and congestion problems in satellite-terrestrial multicast networks. We present a satellite-terrestrial network architecture with the Software-Defined Networking (SDN) features to offer dynamic multicast services for terrestrial users. A Two-Layered Shared Tree Multicast (TSTM) routing algorithm is proposed to achieve efficient dynamic multicast group management, address the trade-off between bandwidth consumption and rerouting overheads. The algorithm also implements congestion control by using a load factor to reflect on the global network bandwidth usage in routing calculations. This algorithm balances the rerouting frequencies of satellite and terrestrial networks to decrease the rerouting overheads and also reduces the network congestion ratio. The simulation shows TSTM decreases rerouting cost, user time delay, and node congestion ratio compared with the locality-aware multicast approach (LAMA).

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“…Tabu search 57 The algorithm starts with an initial binary representation of the solution x and its fitness evaluated through the objective function similarly as performed in GA. The methodological flow of tabu search is given in Figure 2.…”

Section: Tabu Searchmentioning

confidence: 99%

Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

Bagavathi¹,

Saraniya²

2021

Concurrency and Computation

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Synchronous data processing is often preferred considering the volume of data available and the requirement of processed data. Data volume increase from current technical advancements should be balanced and suitably dealt with efficient feature processing techniques. Feature selection can efficiently improve the concurrency in data processing on a completely uncorrelated and separately processable feature set.Texture analysis is a significant application of image processing that analyses specific patterns in an image. As the volume of image data available for producing better classification models progressively moves to be out of the human-manageable range, it is the automation methods that aid the researchers to manage the data. When the number of samples in the data and the dimensionality of the problem is too high, the purpose of data analysis is often compromised. Feature engineering can be utilized to improve the efficiency of the model with a smaller subset of features. This work proposes dibyhrid bio-inspired computation based feature compaction, that is a combination of improvised genetic algorithm and selective tabu search for texture classification problem.Machine learning techniques are employed to evaluate and minimize the validation error from the applied computational methods.

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Tabu Search Algorithm (TSA): A Comprehensive Survey

Cited by 42 publications

References 15 publications

An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

A Two-Layered Shared Tree Multicast Routing Algorithm for Software Defined Hybrid Satellite-Terrestrial Communication Networks

Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

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