Efficient scheduling is critical for the effective use of heterogeneous unmanned aerial vehicles (UAVs) equipped with various sensors. The collaborative “electronic signal guided imaging” reconnaissance mode is first discussed in this article, where electronic signal reconnaissance equipment is initially used to locate electromagnetic targets over long distances, offering approximate target locations that can guide the next imaging reconnaissance. We construct a multiobjective optimization model for heterogeneous multi-UAV coordinated scheduling that minimizes the total travel cost and maximizes the overall weight of scheduled tasks simultaneously. A local-search-based multiobjective algorithm, called multiobjective variable neighborhood descent, is proposed to solve the problem. Specifically, a customized variable neighborhood descent is designed to optimize each objective in parallel, which contains several sophisticatedly designed problem-specific heuristics selected by adaptive weight adjustment. In addition, Metropolis acceptance criteria, which employ adaptive temperature control, are incorporated to prevent premature convergence and improve optimization capability. Furthermore, knee solutions and boundary solutions are optimized specifically to further enhance the optimization. Various local search strategies for two objectives are implemented in an iterative manner until the predefined stopping criteria are satisfied. The proposed algorithm is tested on 18 benchmark instances and one real-world instance. The experimental results on benchmark instances show the superiority of our proposed algorithm with regard to diversity and convergence compared with seven effective algorithms, especially for large-scale instances. The effectiveness and practicability of our proposed algorithm are verified by an additional result on a real-world instance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multiobjective Variable Neighborhood Descent for Heterogeneous Multi-UAV Coordinated Scheduling


    Beteiligte:
    Liu, Huan (Autor:in) / Wu, Guohua (Autor:in) / Ji, Bin (Autor:in) / Wang, Ling (Autor:in)


    Erscheinungsdatum :

    2024-04-01


    Format / Umfang :

    2362377 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A variable MIP neighborhood descent for the multi-attribute inventory routing problem

    Coelho, Leandro Callegari / De Maio, Annarita / Laganà, Demetrio | Elsevier | 2020



    A Variable Neighborhood Descent Algorithm for the Undirected Capacitated Arc Routing Problem

    Hertz, A. / Mittaz, M. | British Library Online Contents | 2001


    Multiobjective Sequence Design via Gradient Descent Methods

    Baden, John Michael / O'Donnell, Brian / Schmieder, Lance | IEEE | 2018


    Generalized Multiobjective Evolutionary Algorithm Guided by Descent Directions

    Denysiuk, R. / Costa, L. / Santo, I. E. | British Library Online Contents | 2014