Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning

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dc.contributor.authorRamirez Atencia, Cristian
dc.contributor.authorDel Ser, Javier
dc.contributor.authorCamacho, David
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:10:39Z
dc.date.available2024-07-24T12:10:39Z
dc.date.issued2019-02
dc.descriptionPublisher Copyright: © 2018 Elsevier B.V.
dc.description.abstractManagement and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.en
dc.description.sponsorshipThis work has been co-funded by: Savier Project (Airbus Defence & Space, FUAM-076915 ), Spanish Ministry of Science and Education and Competitivity (MINECO) and European Regional Development Fund (FEDER) under projects EphemeCH ( TIN2014-56494-C4-4-P ), and DeepBio ( TIN2017-85727-C4-3-P ), Comunidad Autónoma de Madrid under project CIBERDINE S2013/ICE-3095 , and the Basque Government for its support through the EMAITEK program. The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: José Insenser, César Castro and Gemma Blasco. This work has been co-funded by: Savier Project (Airbus Defence & Space, FUAM-076915), Spanish Ministry of Science and Education and Competitivity (MINECO) and European Regional Development Fund (FEDER) under projects EphemeCH (TIN2014-56494-C4-4-P), and DeepBio (TIN2017-85727-C4-3-P), Comunidad Aut?noma de Madrid under project CIBERDINE S2013/ICE-3095, and the Basque Government for its support through the EMAITEK program. The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: Jos? Insenser, C?sar Castro and Gemma Blasco.
dc.description.statusPeer reviewed
dc.format.extent16
dc.identifier.citationRamirez Atencia , C , Del Ser , J & Camacho , D 2019 , ' Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning ' , Swarm and Evolutionary Computation , vol. 44 , pp. 480-495 . https://doi.org/10.1016/j.swevo.2018.06.005
dc.identifier.doi10.1016/j.swevo.2018.06.005
dc.identifier.issn2210-6502
dc.identifier.urihttps://hdl.handle.net/11556/4099
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85048350786&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofSwarm and Evolutionary Computation
dc.relation.projectIDAirbus Defence & Space, FUAM-076915
dc.relation.projectIDSavier Open Innovation
dc.relation.projectIDSpanish Ministry of Science and Education and Competitivity
dc.relation.projectIDComunidad de Madrid, CIBERDINE S2013/ICE-3095
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDMinisterio de Economía y Competitividad, MINECO
dc.relation.projectIDEuropean Regional Development Fund, FEDER, TIN2014-56494-C4-4-P-TIN2017-85727-C4-3-P
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsConstraint satisfaction problem
dc.subject.keywordsMission planning
dc.subject.keywordsMulti-objective evolutionary algorithm
dc.subject.keywordsUnmanned air vehicle
dc.subject.keywordsWeighted strategies
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsGeneral Mathematics
dc.titleWeighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planningen
dc.typejournal article
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