A probabilistic tree-based representation for non-convex minimum cost flow problems

Ghasemishabankareh, B, Ozlen, M, Neumann, F and Li, X 2018, 'A probabilistic tree-based representation for non-convex minimum cost flow problems', in Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN'2018), Coimbra, Portugal, 8 - 12 September 2018, pp. 69-81.


Document type: Conference Paper
Collection: Conference Papers

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Title A probabilistic tree-based representation for non-convex minimum cost flow problems
Author(s) Ghasemishabankareh, B
Ozlen, M
Neumann, F
Li, X
Year 2018
Conference name PPSN'2018
Conference location Coimbra, Portugal
Conference dates 8 - 12 September 2018
Proceedings title Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN'2018)
Publisher Springer Nature
Place of publication Cham, Switzerland
Start page 69
End page 81
Total pages 13
Abstract Network flow optimisation has many real-world applications. The minimum cost flow problem (MCFP) is one of the most common network flow problems. Mathematical programming methods often assume the linearity and convexity of the underlying cost function, which is not realistic in many real-world situations. Solving large-sized MCFPs with nonlinear non-convex cost functions poses a much harder problem. In this paper, we propose a new representation scheme for solving non-convex MCFPs using genetic algorithms (GAs). The most common representation scheme for solving the MCFP in the literature using a GA is priority-based encoding, but it has some serious limitations including restricting the search space to a small part of the feasible set. We introduce a probabilistic tree-based representation scheme (PTbR) that is far superior compared to the priority-based encoding. Our extensive experimental investigations show the advantage of our encoding compared to previous methods for a variety of cost functions.
Subjects Neural, Evolutionary and Fuzzy Computation
Optimisation
Operations Research
Keyword(s) Representation scheme
Genetic algorithm
Minimum cost flow problem
Mixed integer nonlinear programming
DOI - identifier 10.1007/978-3-319-99253-2_6
Copyright notice © Springer Nature Switzerland AG 2018
ISBN 9783319992525
Additional Notes The final authenticated version is available online at https://doi.org/10.1007/978-3-319-99253-2_6
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