Survey of Methods of Solving TSP along with its Implementation using Dynamic Programming Approach

Chauhan, Chetan Singh; Gupta, Ravindra Kumar; Pathak, Kshitij

doi:10.5120/8189-1550

Cited by 40 publications

(20 citation statements)

References 24 publications

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“…To speed up the sorting process, the pairwise edit distances are calculated in a pre‐process, not only for each pair of segmentations, but also separately for each label. Moreover, as TSP is NP‐hard, we utilize a nearest neighbourhood heuristic to find an approximate solution of the TSP efficiently [CGP12]. This way, the similarity‐based sorting can be performed in less than a second, allowing analysts to explore different parameter dependencies without annoying delays.…”

Section: Correlation‐driven Interactive Explorationmentioning

confidence: 99%

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Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

Eichner

Schumann

Tominski

2019

Computer Graphics Forum

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This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.

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Section: Correlation‐driven Interactive Explorationmentioning

confidence: 99%

“…To address this issue, future work could study more closely the fact that the TSP to be solved is symmetric and metric. Alternative solution strategies tailored to this specific class of TSPs might lead to improved sorting results, while maintaining short calculation times [CGP12].…”

Section: Ease Of Usementioning

confidence: 99%

Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

Eichner

Schumann

Tominski

2019

Computer Graphics Forum

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“…Dynamic programming is a technique for efficiently computing recurrences by storing partial results and re-using them when needed [21]. This method is proposed by Balas in 1996, it is well known that dynamic-programming recursions can be expressed as shortest-path problems in a layered network whose nodes correspond to the states of the dynamic program.…”

Section: Comparison With Dynamic Programmingmentioning

confidence: 99%

TSP Solver using Constructive Method of Heuristic Approach

Chauhan¹,

Gupta²,

Pathak³

2012

IJCA

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The Traveling salesperson problem (TSP) is one of the problem in mathematics and computer science which had drown attention as it is easy to understand and difficult to solve. In this paper, we implemented a heuristic approach for TSP using constructive method which generates satisfactory results in asymptotically linear time. Earlier work consider complete graph as a input to TSP. TSP solver generated by proposed approach can work with non-complete graph as well as complete graph.

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“…TSP was used as a benchmark for many optimization problems to find out the optimal solutions with minimum cost [2]. All known exact methods for TSP such as dynamic programming are time consuming and require an exponentially increasing amount of time; which takes an order of O(n 2 2 n ) [3].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Discrete Firefly Algorithm for Solving the Traveling Salesman Problem

Mohsen

Al-Sorori

2017

Applied Computing and Information Technology

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Firefly algorithm is a new meta-heuristic inspired by a natural phenomenon of fireflies' flashing light. Firefly algorithm has been successfully applied to solve several optimization problems. However, It still suffers from some drawbacks such as easily getting stuck at local optima and slow speed of convergence. This paper proposes a new hybrid variant of discrete firefly algorithm, called HDFA, to solve traveling salesman problem (TSP). In the proposed improvement, the balance between intensification and diversification is achieved by utilizing the local search procedures, 2-opt and 3-opt, to improve searching performance and speed up the convergence. In addition, the genetic algorithm operators, crossover and mutation, are added to allow performance of both local and global search respectively. The validity of HDFA is verified by comparative experiments using eighteen TSP benchmark instances from TSBLIB and compared to some well-known algorithms. Results in the conducted experiments show that HDFA has significantly better performance than the performance of compared algorithms for all instances in terms of solution quality.

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Survey of Methods of Solving TSP along with its Implementation using Dynamic Programming Approach

Cited by 40 publications

References 24 publications

Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

TSP Solver using Constructive Method of Heuristic Approach

A New Hybrid Discrete Firefly Algorithm for Solving the Traveling Salesman Problem

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