Solving multi-objective dynamic travelling salesman problems by relaxation

Ricciardi, Lorenzo A. and Vasile, Massimiliano; López-Ibáñez, Manuel, ed. (2019) Solving multi-objective dynamic travelling salesman problems by relaxation. In: GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, CZE, pp. 1999-2007. ISBN 9781450367486

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    Abstract

    This paper describes a method to solve Multi-objective Dynamic Travelling Salesman Problems. The problems are formulated as multi-objective hybrid optimal control problems, where the choice of the target destination for each phase is an integer variable. The resulting problem has thus a combinatorial nature in addition to being a multi-objective optimal control problem. The overall solution approach is based on a combination of the Multi Agent Collaborative Search, a population based memetic multi-objective optimisation algorithm, and the Direct Finite Elements Transcription, a direct method for optimal control problems. A relaxation approach is employed to transform the mixed integer problem into a purely continuous problem, and a set of smooth constraints is added in order to ensure that the relaxed variables of the final solution assume an integer value. A special set of smooth constraints is introduced in order to treat the mutually exclusive choices of the targets for each phase. The method is tested on two problems: the first is a motorised Travelling salesman problem described in the literature, the second is a space application where a satellite has to de-orbit multiple debris. For the first problem, the approach is generating better solutions than those reported in the literature.

    Creators(s): Ricciardi, Lorenzo A. ORCID logoORCID: https://orcid.org/0000-0002-0895-7961 and Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465; López-Ibáñez, Manuel
    Item type:Book Section
    ID code:68878
    Keywords:multi-objective optimisation, global optimisation, optimal control, mixed integer nonlinear programming, memetic algorithms, aerospace, Motor vehicles. Aeronautics. Astronautics, Aerospace Engineering
    Subjects:Technology > Motor vehicles. Aeronautics. Astronautics
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Strategic Research Themes > Ocean, Air and Space
    Depositing user: Pure Administrator
    Date deposited:17 Jul 2019 10:30
    Last modified:24 May 2020 01:15
    URI:https://strathprints.strath.ac.uk/id/eprint/68878
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