Large-scale mission scheduling for multiple heterogeneous agile earth observation satellites using modified chameleon swarm algorithm

Large-scale mission scheduling for multiple heterogeneous agile earth observation satellites using modified chameleon swarm algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Large-scale mission scheduling for multiple heterogeneous agile earth observation satellites using modified chameleon swarm algorithm Chao Zhang, Yunfeng Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3774358/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The large-scale mission scheduling problem for multiple heterogeneous agile earth observation satellites is generally considered an extremely complex NP problem, which usually requires a large amount of computing resources to obtain the optimal solution or near-optimal solution. In order to solve the problem, the original chameleon swarm algorithm (CSA) is chosen to be modified from four aspects to improve its computational efficiency and global search ability. Specifically, the multi-population mechanism is introduced into the hunting prey of the modified chameleon swarm algorithm (MCSA), where the search strategies of three sub-populations and two different emergency response strategies are designed. The conflict elimination strategy based on heuristic rules is designed for the invalid solutions generated during the iteration process. Besides, the screening mechanism and the improved coding method are introduced into the MCSA based on the existing references. The results show that compared with the original CSA and two other existing referenced modified algorithms, the MCSA has significant advantages in both the computational efficiency and the global search ability, which verified the effectiveness of MCSA. chameleon swarm algorithm multi-population mechanism conflict elimination strategy mission scheduling multiple heterogeneous agile earth observation satellites Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3774358","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":261695823,"identity":"d7131314-bb22-4993-b7f1-eb7db0186a4b","order_by":0,"name":"Chao Zhang","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Zhang","suffix":""},{"id":261695824,"identity":"0b01273c-1604-4ddc-88fd-c33111ed5b8b","order_by":1,"name":"Yunfeng Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZgglZ4DCJUKLgTEJWiDAIHED0Vrk23kPPvxR8yd9u0Tyww8MFdaJDexnD+A3/jBfsjHPMYPcnTPSjCUYzqQnNvDkJeDXwsxjJs3AZpC74UaCgQRj2+HEBgkeA/wOa+Yxk/zxzyDd4Eb65x+M/4jQwnCYx0yCt80gweBGjpkEYwMRWgwO8xgb8/YZG+7seVNmkXAs3biNJ4eAw/rPGD788U1O3pw9ffONDzXWsv3sZwg4DAUkADEbCepHwSgYBaNgFOAAAGoTPouIalifAAAAAElFTkSuQmCC","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2023-12-19 01:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3774358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3774358/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48824372,"identity":"00cb0700-f9c3-4913-8d61-3071188c9fdd","added_by":"auto","created_at":"2023-12-26 23:22:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":861512,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3774358/v1_covered_3f213fa1-7f35-4a52-b514-37e94138dd5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Large-scale mission scheduling for multiple heterogeneous agile earth observation satellites using modified chameleon swarm algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chameleon swarm algorithm, multi-population mechanism, conflict elimination strategy, mission scheduling, multiple heterogeneous agile earth observation satellites","lastPublishedDoi":"10.21203/rs.3.rs-3774358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3774358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The large-scale mission scheduling problem for multiple heterogeneous agile earth observation satellites is generally considered an extremely complex NP problem, which usually requires a large amount of computing resources to obtain the optimal solution or near-optimal solution. 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