Learning Adaptive Genetic Algorithm for Earth Electromagnetic Satellite Scheduling

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dc.authoridXing, Lining/0000-0002-6983-4244
dc.authoridyang, qinwen/0000-0002-6762-1528
dc.authoridOu, Junwei/0000-0001-8769-0953
dc.authoridSuganthan, Ponnuthurai Nagaratnam/0000-0003-0901-5105
dc.authorwosidSuganthan, Ponnuthurai Nagaratnam/A-5023-2011
dc.contributor.authorSong, Yanjie
dc.contributor.authorOu, Junwei
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnam
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYang, Qinwen
dc.contributor.authorXing, Lining
dc.date.accessioned2024-05-19T14:46:35Z
dc.date.available2024-05-19T14:46:35Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractEarth electromagnetic exploration satellites are widely used in many fields due to their wide detection range and high detection sensitivity. The complex environment and the proliferating number of satellites make management a primary issue. In this article, a learning adaptive genetic algorithm (LAGA) is proposed for the Earth electromagnetic satellite scheduling problem (EESSP). Control parameters are essential to the successful performance of evolutionary algorithms, and their sensitivity to the problem makes tuning parameters very time-consuming. In the LAGA, a gated recurrent unit (GRU) neural network model is used to control the parameters of variation operators. The neural network model is capable of leveraging real-time information to achieve dynamic parameter adjustment during population search. Moreover, a policy gradient-based reinforcement learning method is utilized to update the parameters of GRU. An adaptive evolution mechanism is employed in LAGA for the autonomous selection of crossover operators. Additionally, the heuristic initialization method, elite strategy, and local search method are incorporated into LAGA to enhance overall performance. Simulation experiments demonstrate the effectiveness of LAGA in solving the EESSP. This study highlights the advantages of utilizing reinforcement learning to optimize neural network models for controlling genetic algorithm searches. Learning adaptive planning methods can effectively address complex problem scenarios and enhance satellite scheduling system performance.en_US
dc.description.sponsorshipScience and Technology Innovation Team of Shaanxi Provinceen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TAES.2023.3312626
dc.identifier.endpage9025en_US
dc.identifier.issn0018-9251
dc.identifier.issn1557-9603
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85171594662en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage9010en_US
dc.identifier.urihttps://doi.org10.1109/TAES.2023.3312626
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5555
dc.identifier.volume59en_US
dc.identifier.wosWOS:001162264000003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Aerospace and Electronic Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSatellitesen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectTask Analysisen_US
dc.subjectReinforcement Learningen_US
dc.subjectApproximation Algorithmsen_US
dc.subjectAdaptation Modelsen_US
dc.subjectSchedulingen_US
dc.subjectControl Parametersen_US
dc.subjectEarth Electromagnetic Satellite Schedulingen_US
dc.subjectGated Recurrent Unit (Gru)en_US
dc.subjectGenetic Algorithm (Ga)en_US
dc.subjectLearning Adaptiveen_US
dc.subjectReinforcement Learningen_US
dc.titleLearning Adaptive Genetic Algorithm for Earth Electromagnetic Satellite Schedulingen_US
dc.typeArticleen_US

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