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Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems

  • Jakob BossekEmail author
  • Christian Grimme
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)

Abstract

The \(\mathcal {NP}\)-hard multi-criteria shortest path problem (mcSPP) is of utmost practical relevance, e. g., in navigation system design and logistics. We address the problem of approximating the Pareto-front of the mcSPP with sum objectives. We do so by proposing a new mutation operator for multi-objective evolutionary algorithms that solves single-objective versions of the shortest path problem on subgraphs. A rigorous empirical benchmark on a diverse set of problem instances shows the effectiveness of the approach in comparison to a well-known mutation operator in terms of convergence speed and approximation quality. In addition, we glance at the neighbourhood structure and similarity of obtained Pareto-optimal solutions and derive promising directions for future work.

Notes

Acknowledgments

The authors acknowledge support from the European Research Center for Information Systems (ERCIS).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information Systems and StatisticsUniversity of MünsterMünsterGermany

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