Evolving diverse sets of tours for the travelling salesperson problem

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.

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The Perception of Minimal Structures: Performance on Open and Closed Versions of Visually Presented Euclidean Travelling Salesperson Problems

Perception ◽

10.1068/p3416 ◽

2003 ◽

Vol 32 (7) ◽

pp. 871-886 ◽

Cited By ~ 22

Author(s):

Douglas Vickers ◽

Pierre Bovet ◽

Michael D Lee ◽

Peter Hughes

Keyword(s):

Minimal Length ◽

Nearest Neighbour ◽

Optimal Solutions ◽

Response Uncertainty ◽

Structure And Motion ◽

Nearest Neighbours ◽

And Performance ◽

Computational Intractability ◽

Travelling Salesperson Problem

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.

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PathGame: Crowdsourcing Time-Constrained Human Solutions for the Travelling Salesperson Problem

Computational Intelligence and Neuroscience ◽

10.1155/2019/2351591 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9

Author(s):

Slaviša Dumnić ◽

Đorđije Dupljanin ◽

Vladimir Božović ◽

Dubravko Ćulibrk

Keyword(s):

Decision Making ◽

Online Games ◽

Initial Study ◽

Decision Making Process ◽

Human Decision ◽

Travelling Salesperson Problem

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.

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