Bio-Inspired Optimization Algorithm Associated with Reinforcement Learning for Multi-Objective Operating Planning in Radioactive Environment

This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is convert...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Biomimetics (Basel, Switzerland) Jg. 9; H. 7; S. 438
Hauptverfasser:	Kong, Shihan, Wu, Fang, Liu, Hao, Zhang, Wei, Sun, Jinan, Wang, Jian, Yu, Junzhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 17.07.2024
Schlagworte:	Algorithms bio-inspired optimization algorithm combinatorial algorithm Genetic algorithms Heuristic improved genetic algorithm Integer programming Mutation Optimization algorithms Optimization techniques Planning Radiation radioactive environment planning Reinforcement reinforcement learning Robots Traveling salesman problem combinatorial algorithm radioactive environment planning improved genetic algorithm bio-inspired optimization algorithm reinforcement learning
ISSN:	2313-7673, 2313-7673
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future.
AbstractList	This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future. This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future.This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future.
Author	Liu, Hao Wang, Jian Sun, Jinan Kong, Shihan Zhang, Wei Yu, Junzhi Wu, Fang
Author_xml	– sequence: 1 givenname: Shihan orcidid: 0000-0002-6714-1313 surname: Kong fullname: Kong, Shihan – sequence: 2 givenname: Fang surname: Wu fullname: Wu, Fang – sequence: 3 givenname: Hao surname: Liu fullname: Liu, Hao – sequence: 4 givenname: Wei surname: Zhang fullname: Zhang, Wei – sequence: 5 givenname: Jinan surname: Sun fullname: Sun, Jinan – sequence: 6 givenname: Jian orcidid: 0000-0003-3742-9671 surname: Wang fullname: Wang, Jian – sequence: 7 givenname: Junzhi orcidid: 0000-0002-6347-572X surname: Yu fullname: Yu, Junzhi
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39056879$$D View this record in MEDLINE/PubMed
BookMark	eNp9ksFu1DAQQCNUREvpD3BAkbhwCdixN7aPS9XCSosWVXC2HGeyzCqxF9spgm_oR-PdtAgViZOt8Ztnj2eeFyfOOyiKl5S8ZUyRdy36EUdIaKMignAmnxRnNaOsEo1gJ3_tT4uLGHeEEKqaBefkWXGaBYtGCnVW3L1HX61c3GOArtzsE474yyT0rlwOWx8wfRvLZYzeokmZ-JED5Q2g632wMIJL5RpMcOi2ZQ6Vn6YhYbVpd2AT3kI2Qsi6fPp5MO6IoStvTIfezMSVu8Xg3UH1onjamyHCxf16Xny9vvpy-bFabz6sLpfrynJOU6VaDspSULWooWNG9opB3SjFm87UTFGwsjcMeGdAUGUs72mrBAfWycwu2Hmxmr2dNzu9Dzia8FN7g_oY8GGrTcg_O4BuaiWb1tqG23xrR5SR1HJq6m6R44pl15vZtQ_--wQx6RGjhSFXC36KmhHJhRC1Ehl9_Qjd-Sm4XOlMMSXFgXp1T03tCN2f5z30LANyBmzwMQbotcV0bFkKBgdNiT5MiP53QnJq_Sj1wf6fpN-Fg8TT
CitedBy_id	crossref_primary_10_3390_biomimetics9100643
Cites_doi	10.1016/j.knosys.2022.110144 10.1016/j.net.2023.09.012 10.1016/j.jocs.2021.101454 10.1016/j.nucengdes.2017.11.006 10.1016/j.anucene.2015.04.019 10.1016/j.anucene.2018.01.007 10.1109/ICCSCE.2018.8684963 10.1016/j.pnucene.2023.104651 10.31181/dmame622023644 10.1007/s00500-017-2760-y 10.1016/j.cor.2023.106249 10.1016/S0377-2217(99)00284-2 10.1007/978-3-030-34135-0_13 10.1016/j.coche.2022.100878 10.1016/j.jenvrad.2023.107270 10.1016/j.net.2021.05.038 10.1287/ijoc.3.4.376 10.3390/biomimetics8080574 10.1016/j.pnucene.2021.104076 10.1016/j.cie.2020.106778 10.1016/j.cor.2023.106455 10.3390/sym15112048 10.1007/s00521-017-2880-4 10.1109/ICACI58115.2023.10146181 10.1016/j.net.2023.02.005 10.3390/biomimetics8020238 10.1016/j.cor.2021.105400 10.1609/aaai.v37i8.26120
ContentType	Journal Article
Copyright	2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION NPM 8FE 8FH ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 DOA
DOI	10.3390/biomimetics9070438
DatabaseName	CrossRef PubMed ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central ProQuest Central Student SciTech Premium Collection Biological Sciences Biological Science Database Proquest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Biological Science Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection Biological Science Database ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic
DatabaseTitleList	PubMed Publicly Available Content Database CrossRef MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Anatomy & Physiology
EISSN	2313-7673
ExternalDocumentID	oai_doaj_org_article_62986bcc64cb4ed09a81c41a2d586b93 39056879 10_3390_biomimetics9070438
Genre	Journal Article
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 62203015, 62233001, 62203436, 62273351 – fundername: Beijing Natural Science Foundation grantid: 4242038
GroupedDBID	53G 8FE 8FH AADQD AAFWJ AAYXX ABDBF ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS BBNVY BCNDV BENPR BHPHI CCPQU CITATION GROUPED_DOAJ HCIFZ HYE IAO IHR INH ITC LK8 M7P MODMG M~E OK1 PGMZT PHGZM PHGZT PIMPY PQGLB PROAC RPM NPM ABUWG AZQEC DWQXO GNUQQ PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO
ID	FETCH-LOGICAL-c441t-9b4e9c1e9272ed3a8f93e269946da2391ec8fa3e4dae719ac4f1b974e3d88f953
IEDL.DBID	DOA
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001276568700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2313-7673
IngestDate	Fri Oct 03 12:51:03 EDT 2025 Thu Oct 02 11:37:43 EDT 2025 Fri Jul 25 11:50:18 EDT 2025 Mon Jul 21 06:08:55 EDT 2025 Tue Nov 18 20:58:02 EST 2025 Sat Nov 29 07:13:48 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	7
Keywords	combinatorial algorithm radioactive environment planning improved genetic algorithm bio-inspired optimization algorithm reinforcement learning
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c441t-9b4e9c1e9272ed3a8f93e269946da2391ec8fa3e4dae719ac4f1b974e3d88f953
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-6347-572X 0000-0003-3742-9671 0000-0002-6714-1313
OpenAccessLink	https://doaj.org/article/62986bcc64cb4ed09a81c41a2d586b93
PMID	39056879
PQID	3084739877
PQPubID	2055439
ParticipantIDs	doaj_primary_oai_doaj_org_article_62986bcc64cb4ed09a81c41a2d586b93 proquest_miscellaneous_3084777297 proquest_journals_3084739877 pubmed_primary_39056879 crossref_citationtrail_10_3390_biomimetics9070438 crossref_primary_10_3390_biomimetics9070438
PublicationCentury	2000
PublicationDate	2024-Jul-17
PublicationDateYYYYMMDD	2024-07-17
PublicationDate_xml	– month: 07 year: 2024 text: 2024-Jul-17 day: 17
PublicationDecade	2020
PublicationPlace	Switzerland
PublicationPlace_xml	– name: Switzerland – name: Basel
PublicationTitle	Biomimetics (Basel, Switzerland)
PublicationTitleAlternate	Biomimetics (Basel)
PublicationYear	2024
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Chao (ref_9) 2018; 115 Alipour (ref_28) 2018; 30 ref_14 Mazyavkina (ref_25) 2021; 134 ref_30 Zheng (ref_23) 2023; 157 Chen (ref_24) 2020; 149 Yasear (ref_29) 2021; 14 Mahmoudinazlou (ref_22) 2024; 162 ref_18 ref_17 Zhang (ref_2) 2023; 159 ref_15 Liu (ref_8) 2015; 83 Reinelt (ref_27) 1991; 3 Toaza (ref_13) 2023; 148 Rehm (ref_1) 2023; 39 Adibel (ref_4) 2021; 53 Zheng (ref_16) 2023; 260 Xie (ref_6) 2022; 144 Mzili (ref_20) 2023; 6 ref_21 Helsgaun (ref_12) 2000; 126 Lee (ref_11) 2024; 56 Zhang (ref_10) 2023; 55 Hatamlou (ref_31) 2018; 22 Panwar (ref_19) 2021; 55 ref_26 Pentreath (ref_3) 2023; 270 Wang (ref_5) 2018; 326 ref_7
References_xml	– volume: 260 start-page: 110144 year: 2023 ident: ref_16 article-title: Reinforced Lin–Kernighan–Helsgaun algorithms for the traveling salesman problems publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2022.110144 – volume: 56 start-page: 92 year: 2024 ident: ref_11 article-title: A proposal on multi-agent static path planning strategy for minimizing radiation dose publication-title: Nucl. Eng. Technol. doi: 10.1016/j.net.2023.09.012 – volume: 55 start-page: 101454 year: 2021 ident: ref_19 article-title: Transformation operators based grey wolf optimizer for travelling salesman problem publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2021.101454 – volume: 326 start-page: 79 year: 2018 ident: ref_5 article-title: The path-planning in radioactive environment of nuclear facilities using an improved particle swarm optimization algorithm publication-title: Nucl. Eng. Des. doi: 10.1016/j.nucengdes.2017.11.006 – volume: 83 start-page: 161 year: 2015 ident: ref_8 article-title: Minimum dose method for walking-path planning of nuclear facilities publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2015.04.019 – volume: 115 start-page: 73 year: 2018 ident: ref_9 article-title: Grid-based RRT* for minimum dose walking path-planning in complex radioactive environments publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2018.01.007 – ident: ref_30 doi: 10.1109/ICCSCE.2018.8684963 – volume: 159 start-page: 104651 year: 2023 ident: ref_2 article-title: Hybrid IACO-A-PSO optimization algorithm for solving multiobjective path planning problem of mobile robot in radioactive environment publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2023.104651 – volume: 6 start-page: 150 year: 2023 ident: ref_20 article-title: Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem publication-title: Decis. Mak. Appl. Manag. Eng. doi: 10.31181/dmame622023644 – volume: 22 start-page: 8167 year: 2018 ident: ref_31 article-title: Solving travelling salesman problem using black hole algorithm publication-title: Soft Comput. doi: 10.1007/s00500-017-2760-y – volume: 157 start-page: 106249 year: 2023 ident: ref_23 article-title: A reinforced hybrid genetic algorithm for the traveling salesman problem publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2023.106249 – ident: ref_14 – volume: 126 start-page: 106 year: 2000 ident: ref_12 article-title: An effective implementation of the Lin-Kernighan traveling salesman heuristic publication-title: Eur. J. Oper. Res. doi: 10.1016/S0377-2217(99)00284-2 – volume: 148 start-page: 110908 year: 2023 ident: ref_13 article-title: A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems publication-title: Eur. J. Oper. Res. – ident: ref_17 doi: 10.1007/978-3-030-34135-0_13 – volume: 14 start-page: 136 year: 2021 ident: ref_29 article-title: Fine-tuning the ant colony system algorithm through Harris’s hawk optimizer for travelling salesman problem publication-title: Int. J. Intell. Eng. Syst. – volume: 39 start-page: 100878 year: 2023 ident: ref_1 article-title: Advanced nuclear energy: The safest and most renewable clean energy publication-title: Curr. Opin. Chem. Eng. doi: 10.1016/j.coche.2022.100878 – volume: 270 start-page: 107270 year: 2023 ident: ref_3 article-title: Radiological protection, radioecology, and the protection of animals in high-dose exposure situations publication-title: J. Environ. Radioact. doi: 10.1016/j.jenvrad.2023.107270 – volume: 53 start-page: 3505 year: 2021 ident: ref_4 article-title: Path planning in nuclear facility decommissioning: Research status, challenges, and opportunities publication-title: Nucl. Eng. Technol. doi: 10.1016/j.net.2021.05.038 – volume: 3 start-page: 376 year: 1991 ident: ref_27 article-title: TSPLIB-A traveling salesman problem library publication-title: ORSA J. Comput. doi: 10.1287/ijoc.3.4.376 – ident: ref_21 doi: 10.3390/biomimetics8080574 – volume: 144 start-page: 104076 year: 2022 ident: ref_6 article-title: The multi-objective inspection path-planning in radioactive environment based on an improved ant colony optimization algorithm publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2021.104076 – volume: 149 start-page: 106778 year: 2020 ident: ref_24 article-title: A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2020.106778 – volume: 162 start-page: 106455 year: 2024 ident: ref_22 article-title: A hybrid genetic algorithm for the min–max multiple traveling salesman problem publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2023.106455 – ident: ref_7 doi: 10.3390/sym15112048 – volume: 30 start-page: 2935 year: 2018 ident: ref_28 article-title: A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-2880-4 – ident: ref_26 doi: 10.1109/ICACI58115.2023.10146181 – volume: 55 start-page: 1838 year: 2023 ident: ref_10 article-title: Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A publication-title: Nucl. Eng. Technol. doi: 10.1016/j.net.2023.02.005 – ident: ref_18 doi: 10.3390/biomimetics8020238 – volume: 134 start-page: 105400 year: 2021 ident: ref_25 article-title: Reinforcement learning for combinatorial optimization: A survey publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2021.105400 – ident: ref_15 doi: 10.1609/aaai.v37i8.26120
SSID	ssj0001965440
Score	2.276349
Snippet	This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is...
SourceID	doaj proquest pubmed crossref
SourceType	Open Website Aggregation Database Index Database Enrichment Source
StartPage	438
SubjectTerms	Algorithms bio-inspired optimization algorithm combinatorial algorithm Genetic algorithms Heuristic improved genetic algorithm Integer programming Mutation Optimization algorithms Optimization techniques Planning Radiation radioactive environment planning Reinforcement reinforcement learning Robots Traveling salesman problem
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELZglwMXXsujsCAjIS7I2iR2bM8JtagruHRXFUh7i1zbKUHbZGm7SPwHfjRjx2mFEHvhak8sRzMez8PzDSFvjKlFaGPBfATVxkuEQWkUUxJ07aUN2ZjYbELNZvriAs5TwG2TnlUOOjEqatfZECM_4RnqUY4esnp_9Z2FrlEhu5paaNwmhwGpDOX8cDKdnc_3URaQpRBZXy3D0b8_CVXtzSoUCG7QLwx5sD9upAjc_29rM946p_f_d78PyL1kb9JxLyAPyS3fPiJH4xZ97dVP-pbGF6AxtH5Efk2ajn1qQ-7dO3qGymSVqjTp-HKJi2-_rujAT6QIMVw69xF71cYwI01wrUuKQzQW97KzxbdeqeKKAcE5zA6tkmjT0rlxTWd6ium-7O4x-XI6_fzhI0vdGphFk2rLYCE82NxDoQrvuNE1cF9IACGdKTjk3uracC-c8SoHY0WdL9Cb8dxppC35E3LQdq1_Rihktc5zx8EpIZR1OuOWOwdlXXrBpRmRfOBYZROUeeiocVmhSxO4XP3N5RF5t_vmqgfyuJF6EgRhRxlAuONAt15W6UxXsgAtF9ZKYfHfXQZG51bkpnAljgMfkeNBNKqkGTbVXi5G5PVuGs90SNSY1nfXiSa4PUjztBe_3U5wv6XUCp7fvPgLcrdA84tFCNBjcrBdX_uX5I79sW0261fpmPwGzYwh6A priority: 102 providerName: ProQuest
Title	Bio-Inspired Optimization Algorithm Associated with Reinforcement Learning for Multi-Objective Operating Planning in Radioactive Environment
URI	https://www.ncbi.nlm.nih.gov/pubmed/39056879 https://www.proquest.com/docview/3084739877 https://www.proquest.com/docview/3084777297 https://doaj.org/article/62986bcc64cb4ed09a81c41a2d586b93
Volume	9
WOSCitedRecordID	wos001276568700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2313-7673 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001965440 issn: 2313-7673 databaseCode: DOA dateStart: 20160101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2313-7673 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001965440 issn: 2313-7673 databaseCode: M~E dateStart: 20160101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database (ProQuest) customDbUrl: eissn: 2313-7673 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001965440 issn: 2313-7673 databaseCode: M7P dateStart: 20161201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2313-7673 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001965440 issn: 2313-7673 databaseCode: BENPR dateStart: 20161201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 2313-7673 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001965440 issn: 2313-7673 databaseCode: PIMPY dateStart: 20161201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1fb9MwELdg8MALGow_ZaMyEuIFRUtix_Y9tqgTe6CLKpDKU-TazpZpTdDaTdp32Ife2U7LEAJeeD1fIst39t35fL8j5L3WNfdtLBIXQLXRiCRQaJlIAap2wvhsTGg2IadTNZ9Dea_Vl38TFuGB48IdihyUWBgjuFlwZ1PQKjM807ktkA4B5zOVcC-YOo-gLwXnaaySYRjXH_pq9mbpCwNXGA_6_NcvligA9v_ZywzW5miXPO3dRDqK03tGHrj2OdkbtRgiL2_oBxoeboYb8T1yO2665Lj1KXNn6QmeAcu-uJKOLk47jP7PlnQjBuTwV6905gJkqgm3g7RHWT2lSKKhJjc5WZzHsxD_6IGX_eimwxFtWjrTtul05Jj8rJZ7Qb4dTb5--pz0TRYSg57QOgFcVTCZg1zmzjKtamAuFwBcWJ0zyJxRtWaOW-1kBtrwOltgEOKYVchbsJdkp-1a95pQSGuVZZaBlZxLY1XKDLMWirpwnAk9INlmwSvTI5D7RhgXFUYiXkjV70IakI_bb35E_I2_co-9HLecHjs7EFCjql6jqn9p1IAcbLSg6jf0qmIpmnEGSsoBebcdxq3o8yu6dd1Vz-OjFeR5FbVnOxOcbyGUhDf_Y4b75EmOvlUS8D0PyM768sq9JY_N9bpZXQ7JQzlXQ_JoPJmWs2HYF0P_pLVEWnn8pfx-B_x5F6g
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwELaqLRJc-Cs_WwoYCbggq0nsOPEBoS206qrtdlUVqZxSr-0sW3WTspuC-g48C8_IjJPsCiF664GrPYkS58uMZ8YzHyGvtc4F0lgw55tqgxFhKtYJS6RKcycNZmM82UQyGKQnJ2q4Qn61tTB4rLLViV5R29JgjHyTB6BHOXjIyYeLbwxZozC72lJo1LDYc1c_wGWbv-9_gu_7Jop2to8_7rKGVYAZMP0VUyPhlAmdipLIWa7TXHEXSaWEtDriKnQmzTV3wmqXhEobkYcj2HU7blOQRZYIUPmrAsAedMjqsH8w_LKM6igZCxHU1Tmcq2ATq-gnUyxInIMfinm3PyygJwr49-7WW7mde__b-twnd5v9NO3VP8ADsuKKh2StV-iqnF7Rt9SfcPWpgzXyc2tSsn6BZwucpYegLKdNFSrtnY_hZaqvU9riFSQwRk2PnO8ta3wYlTbtaMcUhqgvXmaHo7PaaMAdsUM1zrZUUHRS0CNtJ6WuJbaXZYWPyOcbWZfHpFOUhXtKqAryNAwtVzYRIjE2Dbjh1qo4j53gUndJ2CIkM02rdmQMOc_AZUNUZX-jqkveLa65qBuVXCu9hcBbSGKTcT9QzsZZo7MyGalUjoyRwsC720DpNDQi1JGNYVzxLtlooZg1mm-eLXHYJa8W06CzMBGlC1deNjLo1oHMkxruiyeB541lmqj162_-ktzePT7Yz_b7g71n5E4EW03m251ukE41u3TPyS3zvZrMZy-aX5SS05tG_W-QMoBL
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3dbtMwFLamgRA3_A1GxwAjATfIamI7jn2BUMdWUQ111QTS7oJrOyVoTbY2A-0deCKejmMnaYUQu9sFt85JFDtfjs_x-fkQeql1zj2NBXGhqTZsIkQlOiWpUDJ3wvhoTCCbSMdjeXKiJhvoV1cL49MqO50YFLWtjD8j77MI9CgDDznt521axGR_-O7snHgGKR9p7eg0Gogcussf4L4t34724Vu_onR48On9B9IyDBADZkBN1JQ7ZWKnaEqdZVrmijkqlOLCaspU7IzMNXPcapfGShuex1OwwB2zEmQ9YwSo_xsw14iGtMHJ-nxHiYTzqKnTYUxFfV9PX8x9aeISPFIfgftjLwyUAf-2c8N-N7z7P6_UPXSntbLxoPkt7qMNVz5AW4NS19X8Er_GIe81BBS20M-9oiKj0mccOIuPQIXO29pUPDidwWTqr3PcoRgk_Mk1Pnah46wJh6u4bVI7wzCEQ0kzOZp-a7YSeKLvW-2vdgRRuCjxsbZFpRuJg3Wx4UP0-VrW5RHaLKvSPUZYRbmMY8uUTTlPjZURM8xaleSJ40zoHoo7tGSmbeDueUROM3DkPMKyvxHWQ29W95w17UuulN7zIFxJ-tbjYaBazLJWk2WCKimmxghuYO42UlrGhsea2gTGFeuh3Q6WWasPl9kakz30YnUZNJkPT-nSVRetjHf2QGa7gf7qTeB9EyFTtXP1w5-jWwD17ONofPgE3aZgf5LQA3UXbdaLC_cU3TTf62K5eBb-VYy-XDfkfwPbOoeT
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bio-Inspired+Optimization+Algorithm+Associated+with+Reinforcement+Learning+for+Multi-Objective+Operating+Planning+in+Radioactive+Environment&rft.jtitle=Biomimetics+%28Basel%2C+Switzerland%29&rft.au=Shihan+Kong&rft.au=Fang+Wu&rft.au=Hao+Liu&rft.au=Wei+Zhang&rft.date=2024-07-17&rft.pub=MDPI+AG&rft.eissn=2313-7673&rft.volume=9&rft.issue=7&rft.spage=438&rft_id=info:doi/10.3390%2Fbiomimetics9070438&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_62986bcc64cb4ed09a81c41a2d586b93
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2313-7673&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2313-7673&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2313-7673&client=summon