The ‘Travelling Salesman Problem’: A New Approach for Identification of Differences among Pollen Allergens

1997 ◽  
Vol 112 (4) ◽  
pp. 371-377 ◽  
Author(s):  
E. Kosman ◽  
A. Eshel ◽  
Y. Waisel
Keyword(s):  
Travelling Salesman Problem ◽  
Travelling Salesman ◽  
Pollen Allergens ◽  
New Approach

scholarly journals Travelling Santa Problem: Optimization of a Million-Households Tour Within One Hour

2021 ◽  
Vol 8 ◽  
Author(s):  
Tilo Strutz
Keyword(s):  
Computational Complexity ◽  
Optimization Problems ◽  
State Of The Art ◽  
Travelling Salesman Problem ◽  
Limited Time ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Major Difficulty ◽  
Travelling Salesman ◽  
New Approach

Finding the shortest tour visiting all given points at least ones belongs to the most famous optimization problems until today [travelling salesman problem (TSP)]. Optimal solutions exist for many problems up to several ten thousand points. The major difficulty in solving larger problems is the required computational complexity. This shifts the research from finding the optimum with no time limitation to approaches that find good but sub-optimal solutions in pre-defined limited time. This paper proposes a new approach for two-dimensional symmetric problems with more than a million coordinates that is able to create good initial tours within few minutes. It is based on a hierarchical clustering strategy and supports parallel processing. In addition, a method is proposed that can correct unfavorable paths with moderate computational complexity. The new approach is superior to state-of-the-art methods when applied to TSP instances with non-uniformly distributed coordinates.

The Travelling Salesman Problem for finite-sized cities

2010 ◽  
Vol 20 (6) ◽  
pp. 1067-1078 ◽  
Author(s):  
HUGO FORT ◽  
MORDECHAI KORNBLUTH ◽  
FREDY ZYPMAN
Keyword(s):  
Travelling Salesman Problem ◽  
Spatial Extent ◽  
Travelling Salesman ◽  
New Approach ◽  
Standard Problem ◽  
First Approximation ◽  
Particular Solution ◽  
New Algorithms

We consider a variation of the Travelling Salesman Problem (TSP) in which the cities visited have non-zero spatial extent, in contrast with the classical TSP, which has destinations that are mathematical points. This new approach opens up both new analyses of the problem and new algorithms for solutions, while remaining an economic first approximation to the standard problem. We present one particular solution that, depending on the number and size of the cities, can improve existing algorithms solving the classical TSP.

scholarly journals A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

2019 ◽  
Vol 2 (3) ◽  
pp. 446-453
Author(s):  
Murat Karakoyun
Keyword(s):  
Shortest Path ◽  
Travelling Salesman Problem ◽  
Experimental Results ◽  
Hard Problem ◽  
Shuffled Frog Leaping Algorithm ◽  
Travelling Salesman ◽  
New Approach ◽  
Starting Point ◽  
Np Hard Problem ◽  
Shuffled Frog Leaping

The Travelling Salesman Problem (TSP), which is a combinatorial NP-hard problem, aims to find the shortest possible path while visiting all cities (only once) in a given list and returns to the starting point. In this paper, an approach, which is based on k-means clustering and Shuffled Frog Leaping Algorithm (SFLA), is used to solve the TSP. The proposed approach consists of three parts: separate the cities into k clusters, find the shortest path for each cluster and merge the clusters. Experimental results have shown that the algorithm get better results as the number of cluster increase for problems that have a large number of cities.

Solving the TSP by Simulated Annealing Genetic Algorithm Based on Google Maps JavaScript API

2011 ◽  
Vol 201-203 ◽  
pp. 733-737
Author(s):  
Xin Biao He ◽  
Yi Wei Mo
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
User Interface ◽  
Travelling Salesman Problem ◽  
Optimal Route ◽  
Travelling Salesman ◽  
Google Maps ◽  
New Approach ◽  
Simulated Annealing Genetic Algorithm

Google Maps JavaScript API enable users calculate directions by using the DirectionsService object. With these directions results, a new approach to solve the Travelling Salesman Problem (TSP) is proposed in this paper. This DirectionsService object communicates with the Google Maps API which receives directions requests and returns computed results. TSP is solved by simulated annealing genetic algorithm (SAGA) with help of returned directions results. In experiment example, the optimal route of the TSP was provided graphically with Google Maps and textually in user interface. The final results demonstrated the feasibility of the proposed approach.

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