Optimization of route bundling via differential evolution with a convex representation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Citations (Scopus)

    Abstract

    Route bundling implies compounding multiple routes in a way that anchoring points at intermediate locations minimize a global distance metric. The result of route bundling is a tree-like structure where the roots of the tree (anchoring points) serve as coordinating locus for the joint transport of information, goods, and people. Route bundling is a relevant conceptual construct in a number of path planning scenarios where the resources and means of transport are scarce/expensive, or where the environments are inherently hard to navigate due to limited space. In this paper we propose a method for searching optimal route bundles based on a self-adaptive class of differential evolution using a convex representation. Computational experiments in scenarios with and without convex obstacles show the feasibility and efficiency of our approach.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages727-732
    Number of pages6
    Volume2017-July
    ISBN (Electronic)9781538620342
    DOIs
    Publication statusPublished - 2018 Mar 9
    Event2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017 - Okinawa, Japan
    Duration: 2017 Jul 142017 Jul 18

    Other

    Other2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
    CountryJapan
    CityOkinawa
    Period17/7/1417/7/18

    ASJC Scopus subject areas

    • Control and Optimization
    • Artificial Intelligence

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  • Cite this

    Parque Tenorio, V., Miura, S., & Miyashita, T. (2018). Optimization of route bundling via differential evolution with a convex representation. In 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017 (Vol. 2017-July, pp. 727-732). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RCAR.2017.8311950