A Strategy Fusion-Based Multiobjective Optimization Approach for Agile Earth Observation Satellite Scheduling Problem

Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has s...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-14
Hauptverfasser: Wang, He, Huang, Weiquan, Magnusson, Sindri, Lindgren, Tony, Wang, Ran, Song, Yanjie
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 14
container_issue
container_start_page 1
container_title IEEE transactions on geoscience and remote sensing
container_volume 62
creator Wang, He
Huang, Weiquan
Magnusson, Sindri
Lindgren, Tony
Wang, Ran
Song, Yanjie
description Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This article tackles a multiobjective AEOS scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a strategy-fused multiobjective dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm's robustness but also underscores its efficiency and speed in solving the MOAEOSSP.
doi_str_mv 10.1109/TGRS.2024.3472749
format Article
fullrecord <record><control><sourceid>swepub_RIE</sourceid><recordid>TN_cdi_ieee_primary_10704720</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10704720</ieee_id><sourcerecordid>oai_DiVA_org_su_237872</sourcerecordid><originalsourceid>FETCH-LOGICAL-c185t-5bf24949a2636742969326235f8de212f01231b5c3d14970a14dec108fd394b03</originalsourceid><addsrcrecordid>eNpNkM1Kw0AURgdRsFYfQHAxD2Dq_OVnlrG2VahUTHU7TJKbdErahJmkUp_elIi4unA551schG4pmVBK5MN68Z5MGGFiwkXIQiHP0Ij6fuSRQIhzNCJUBh6LJLtEV85tCaHCp-EIdTFOWqtbKI943jlT771H7SDHr13VmjrdQtaaA-BV05qd-db9b4_jprG1zja4qC2OS1MBnmnbbvAqdWAPA5T0o1VlWsBJtoG8q8y-xG-2TivYXaOLQlcObn7vGH3MZ-vps7dcLV6m8dLLaOS3np8WTEghNQt4EAomA8lZwLhfRDkwygpCGaepn_GcChkSTUUOGSVRkXMpUsLH6H7YdV_QdKlqrNlpe1S1NurJfMaqtqVynWI8jELW43TAM1s7Z6H4EyhRp8zqlFmdMqvfzL1zNzgGAP7xIekBwn8ACpl6dQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A Strategy Fusion-Based Multiobjective Optimization Approach for Agile Earth Observation Satellite Scheduling Problem</title><source>IEEE Electronic Library (IEL)</source><creator>Wang, He ; Huang, Weiquan ; Magnusson, Sindri ; Lindgren, Tony ; Wang, Ran ; Song, Yanjie</creator><creatorcontrib>Wang, He ; Huang, Weiquan ; Magnusson, Sindri ; Lindgren, Tony ; Wang, Ran ; Song, Yanjie</creatorcontrib><description>Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This article tackles a multiobjective AEOS scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a strategy-fused multiobjective dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm's robustness but also underscores its efficiency and speed in solving the MOAEOSSP.</description><identifier>ISSN: 0196-2892</identifier><identifier>ISSN: 1558-0644</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2024.3472749</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>IEEE</publisher><subject>Agile Earth observation satellite (AEOS) ; Computational modeling ; Computer and Systems Sciences ; data- och systemvetenskap ; Earth ; Energy consumption ; Heuristic algorithms ; Mathematical models ; multiobjective dung beetle optimization (MODBO) ; Optimization ; Processor scheduling ; remote sensing ; satellite observation scheduling ; Satellites ; Scheduling ; Search problems</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2024, Vol.62, p.1-14</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c185t-5bf24949a2636742969326235f8de212f01231b5c3d14970a14dec108fd394b03</cites><orcidid>0000-0001-7713-1381 ; 0000-0002-3780-9343 ; 0000-0002-4313-8312 ; 0009-0005-4417-8622 ; 0009-0006-0293-2153 ; 0000-0002-6617-8683</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10704720$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,4010,27902,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10704720$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237872$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, He</creatorcontrib><creatorcontrib>Huang, Weiquan</creatorcontrib><creatorcontrib>Magnusson, Sindri</creatorcontrib><creatorcontrib>Lindgren, Tony</creatorcontrib><creatorcontrib>Wang, Ran</creatorcontrib><creatorcontrib>Song, Yanjie</creatorcontrib><title>A Strategy Fusion-Based Multiobjective Optimization Approach for Agile Earth Observation Satellite Scheduling Problem</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This article tackles a multiobjective AEOS scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a strategy-fused multiobjective dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm's robustness but also underscores its efficiency and speed in solving the MOAEOSSP.</description><subject>Agile Earth observation satellite (AEOS)</subject><subject>Computational modeling</subject><subject>Computer and Systems Sciences</subject><subject>data- och systemvetenskap</subject><subject>Earth</subject><subject>Energy consumption</subject><subject>Heuristic algorithms</subject><subject>Mathematical models</subject><subject>multiobjective dung beetle optimization (MODBO)</subject><subject>Optimization</subject><subject>Processor scheduling</subject><subject>remote sensing</subject><subject>satellite observation scheduling</subject><subject>Satellites</subject><subject>Scheduling</subject><subject>Search problems</subject><issn>0196-2892</issn><issn>1558-0644</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1Kw0AURgdRsFYfQHAxD2Dq_OVnlrG2VahUTHU7TJKbdErahJmkUp_elIi4unA551schG4pmVBK5MN68Z5MGGFiwkXIQiHP0Ij6fuSRQIhzNCJUBh6LJLtEV85tCaHCp-EIdTFOWqtbKI943jlT771H7SDHr13VmjrdQtaaA-BV05qd-db9b4_jprG1zja4qC2OS1MBnmnbbvAqdWAPA5T0o1VlWsBJtoG8q8y-xG-2TivYXaOLQlcObn7vGH3MZ-vps7dcLV6m8dLLaOS3np8WTEghNQt4EAomA8lZwLhfRDkwygpCGaepn_GcChkSTUUOGSVRkXMpUsLH6H7YdV_QdKlqrNlpe1S1NurJfMaqtqVynWI8jELW43TAM1s7Z6H4EyhRp8zqlFmdMqvfzL1zNzgGAP7xIekBwn8ACpl6dQ</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Wang, He</creator><creator>Huang, Weiquan</creator><creator>Magnusson, Sindri</creator><creator>Lindgren, Tony</creator><creator>Wang, Ran</creator><creator>Song, Yanjie</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>DG7</scope><orcidid>https://orcid.org/0000-0001-7713-1381</orcidid><orcidid>https://orcid.org/0000-0002-3780-9343</orcidid><orcidid>https://orcid.org/0000-0002-4313-8312</orcidid><orcidid>https://orcid.org/0009-0005-4417-8622</orcidid><orcidid>https://orcid.org/0009-0006-0293-2153</orcidid><orcidid>https://orcid.org/0000-0002-6617-8683</orcidid></search><sort><creationdate>2024</creationdate><title>A Strategy Fusion-Based Multiobjective Optimization Approach for Agile Earth Observation Satellite Scheduling Problem</title><author>Wang, He ; Huang, Weiquan ; Magnusson, Sindri ; Lindgren, Tony ; Wang, Ran ; Song, Yanjie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c185t-5bf24949a2636742969326235f8de212f01231b5c3d14970a14dec108fd394b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Agile Earth observation satellite (AEOS)</topic><topic>Computational modeling</topic><topic>Computer and Systems Sciences</topic><topic>data- och systemvetenskap</topic><topic>Earth</topic><topic>Energy consumption</topic><topic>Heuristic algorithms</topic><topic>Mathematical models</topic><topic>multiobjective dung beetle optimization (MODBO)</topic><topic>Optimization</topic><topic>Processor scheduling</topic><topic>remote sensing</topic><topic>satellite observation scheduling</topic><topic>Satellites</topic><topic>Scheduling</topic><topic>Search problems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, He</creatorcontrib><creatorcontrib>Huang, Weiquan</creatorcontrib><creatorcontrib>Magnusson, Sindri</creatorcontrib><creatorcontrib>Lindgren, Tony</creatorcontrib><creatorcontrib>Wang, Ran</creatorcontrib><creatorcontrib>Song, Yanjie</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Stockholms universitet</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, He</au><au>Huang, Weiquan</au><au>Magnusson, Sindri</au><au>Lindgren, Tony</au><au>Wang, Ran</au><au>Song, Yanjie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Strategy Fusion-Based Multiobjective Optimization Approach for Agile Earth Observation Satellite Scheduling Problem</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2024</date><risdate>2024</risdate><volume>62</volume><spage>1</spage><epage>14</epage><pages>1-14</pages><issn>0196-2892</issn><issn>1558-0644</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This article tackles a multiobjective AEOS scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a strategy-fused multiobjective dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm's robustness but also underscores its efficiency and speed in solving the MOAEOSSP.</abstract><pub>IEEE</pub><doi>10.1109/TGRS.2024.3472749</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-7713-1381</orcidid><orcidid>https://orcid.org/0000-0002-3780-9343</orcidid><orcidid>https://orcid.org/0000-0002-4313-8312</orcidid><orcidid>https://orcid.org/0009-0005-4417-8622</orcidid><orcidid>https://orcid.org/0009-0006-0293-2153</orcidid><orcidid>https://orcid.org/0000-0002-6617-8683</orcidid></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 0196-2892
ispartof IEEE transactions on geoscience and remote sensing, 2024, Vol.62, p.1-14
issn 0196-2892
1558-0644
1558-0644
language eng
recordid cdi_ieee_primary_10704720
source IEEE Electronic Library (IEL)
subjects Agile Earth observation satellite (AEOS)
Computational modeling
Computer and Systems Sciences
data- och systemvetenskap
Earth
Energy consumption
Heuristic algorithms
Mathematical models
multiobjective dung beetle optimization (MODBO)
Optimization
Processor scheduling
remote sensing
satellite observation scheduling
Satellites
Scheduling
Search problems
title A Strategy Fusion-Based Multiobjective Optimization Approach for Agile Earth Observation Satellite Scheduling Problem
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T22%3A29%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-swepub_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Strategy%20Fusion-Based%20Multiobjective%20Optimization%20Approach%20for%20Agile%20Earth%20Observation%20Satellite%20Scheduling%20Problem&rft.jtitle=IEEE%20transactions%20on%20geoscience%20and%20remote%20sensing&rft.au=Wang,%20He&rft.date=2024&rft.volume=62&rft.spage=1&rft.epage=14&rft.pages=1-14&rft.issn=0196-2892&rft.eissn=1558-0644&rft.coden=IGRSD2&rft_id=info:doi/10.1109/TGRS.2024.3472749&rft_dat=%3Cswepub_RIE%3Eoai_DiVA_org_su_237872%3C/swepub_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10704720&rfr_iscdi=true