Abstract
This paper focuses on the solving constrained traveling salesman problem with time windows indirectly by finding useful hyper-heuristic via genetic programming. Resulting hyper-heuristic represents calculation of city priority along the traveling salesman path. The influences of different city properties (position in the Cartesian coordinate space, sum of distances to the n nearest cities, polar angle from the beginning of the coordinate system, etc.), math functions (addition, subtraction, division, sine, cosine, tangent) and penalty functions are tested. Trigonometric functions have no positive influence, best results are achieved with Cartesian coordinates and sum of distances to the n nearest cities. Polar angle gives more diverse solutions. Resulting hyper-heuristics are good on training sets. When they are tested on another datasets, they find relatively short paths, but there are problems with respecting constraints in the form of time windows.
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Acknowledgements
The work has been supported by the Funds of University of Pardubice (by project “SGS 2020” No: SGS_2020_011), Czech Republic. This support is very gratefully acknowledged.
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Hrbek, V., Merta, J. (2020). Searching the Hyper-heuristic for the Traveling Salesman Problem with Time Windows by Genetic Programming. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-63322-6_81
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DOI: https://doi.org/10.1007/978-3-030-63322-6_81
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