A Time-Based Formulation and Upper Bounding Scheme for the Selective Travelling Salesperson Problem

1997 ◽  
Vol 48 (5) ◽  
pp. 511
Author(s):  
H. H. Millar ◽  
M. Kiragu
2011 ◽  
Vol 1 (1) ◽  
pp. 88-92
Author(s):  
Pallavi Arora ◽  
Harjeet Kaur ◽  
Prateek Agrawal

Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.


Perception ◽  
10.1068/p3416 ◽  
2003 ◽  
Vol 32 (7) ◽  
pp. 871-886 ◽  
Author(s):  
Douglas Vickers ◽  
Pierre Bovet ◽  
Michael D Lee ◽  
Peter Hughes

The planar Euclidean version of the travelling salesperson problem (TSP) requires finding a tour of minimal length through a two-dimensional set of nodes. Despite the computational intractability of the TSP, people can produce rapid, near-optimal solutions to visually presented versions of such problems. To explain this, MacGregor et al (1999, Perception28 1417–1428) have suggested that people use a global-to-local process, based on a perceptual tendency to organise stimuli into convex figures. We review the evidence for this idea and propose an alternative, local-to-global hypothesis, based on the detection of least distances between the nodes in an array. We present the results of an experiment in which we examined the relationships between three objective measures and performance measures of optimality and response uncertainty in tasks requiring participants to construct a closed tour or an open path. The data are not well accounted for by a process based on the convex hull. In contrast, results are generally consistent with a locally focused process based initially on the detection of nearest-neighbour clusters. Individual differences are interpreted in terms of a hierarchical process of constructing solutions, and the findings are related to a more general analysis of the role of nearest neighbours in the perception of structure and motion.


2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Slaviša Dumnić ◽  
Đorđije Dupljanin ◽  
Vladimir Božović ◽  
Dubravko Ćulibrk

Human strategies for solving the travelling salesperson problem (TSP) continue to draw the attention of the researcher community, both to further understanding of human decision-making and inspiration for the design of automated solvers. Online games represent an efficient way of collecting large amounts of human solutions to the TSP, and PathGame is a game focusing on non-Euclideanclosed-form TSP. To capture the instinctive decision-making process of the users, PathGame requires users to solve the problem as quickly as possible, while still favouring more efficient tours. In the initial study presented here, we have used PathGame to collect a dataset of over 16,000 tours, containing over 22,000,000 destinations. Our analysis of the data revealed new insights related to ways in which humans solve TSP and the time it takes them when forced to solve TSPs of large complexity quickly.


2013 ◽  
Vol 40 (7) ◽  
pp. 1716-1728 ◽  
Author(s):  
Marco Castro ◽  
Kenneth Sörensen ◽  
Pieter Vansteenwegen ◽  
Peter Goos

2008 ◽  
Vol 25 (2) ◽  
pp. 194-217 ◽  
Author(s):  
Simone Cutini ◽  
Andrea Di Ferdinando ◽  
Demis Basso ◽  
Patrizia Silvia Bisiacchi ◽  
Marco Zorzi

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Ziauddin Ursani ◽  
David W. Corne

In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities fromO(n3)toO(n2)with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity ofO(n2). It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics.


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