scholarly journals Approximation algorithms for the traveling salesman problem

2003 ◽  
Vol 56 (3) ◽  
pp. 387-405 ◽  
Author(s):  
Jérôme Monnot ◽  
Vangelis Th. Paschos ◽  
Sophie Toulouse
Keyword(s):  
Approximation Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem

scholarly journals A Graph-Based Approach to Optimal Scan Chain Stitching Using RTL Design Descriptions

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Lilia Zaourar ◽  
Yann Kieffer ◽  
Chouki Aktouf
Keyword(s):  
Approximation Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Graph Models ◽  
Rtl Design ◽  
And Performance ◽  
Scan Chain ◽  
The Traveling Salesman Problem ◽  
The Cost

The scan chain insertion problem is one of the mandatory logic insertion design tasks. The scanning of designs is a very efficient way of improving their testability. But it does impact size and performance, depending on the stitching ordering of the scan chain. In this paper, we propose a graph-based approach to a stitching algorithm for automatic and optimal scan chain insertion at the RTL. Our method is divided into two main steps. The first one builds graph models for inferring logical proximity information from the design, and then the second one uses classic approximation algorithms for the traveling salesman problem to determine the best scan-stitching ordering. We show how this algorithm allows the decrease of the cost of both scan analysis and implementation, by measuring total wirelength on placed and routed benchmark designs, both academic and industrial.

scholarly journals THE TRAVELING SALESMAN PROBLEM FOR LINES AND RAYS IN THE PLANE

2012 ◽  
Vol 04 (04) ◽  
pp. 1250044 ◽  
Author(s):  
ADRIAN DUMITRESCU
Keyword(s):  
Approximation Algorithms ◽  
Traveling Salesman Problem ◽  
Shortest Path ◽  
Linear Time ◽  
Traveling Salesman ◽  
Tight Bound ◽  
Time Approximation ◽  
Minimum Perimeter ◽  
Open Curve ◽  
The Traveling Salesman Problem

In the Euclidean TSP with neighborhoods (TSPN), we are given a collection of n regions (neighborhoods) and we seek a shortest tour that visits each region. In the path variant, we seek a shortest path that visits each region. We present several linear-time approximation algorithms with improved ratios for these problems for two cases of neighborhoods that are (infinite) lines, and respectively, (half-infinite) rays. Along the way we derive a tight bound on the minimum perimeter of a rectangle enclosing an open curve of length L.

A New Heuristic Procedure To Solve The Traveling Salesman Problem

2007 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Paulo Henrique Siqueira ◽  
Sérgio Scheer ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Heuristic Procedure ◽  
The Traveling Salesman Problem

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

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