A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule
This article presents a distributed random search optimization method, the trust region probability collectives (TRPC) method, for unconstrained optimization problems without closed forms. Through analyzing the framework of the original probability collectives (PC) algorithm, three potential require...
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Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2016-04, Vol.20 (4), p.1581-1600 |
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description | This article presents a distributed random search optimization method, the trust region probability collectives (TRPC) method, for unconstrained optimization problems without closed forms. Through analyzing the framework of the original probability collectives (PC) algorithm, three potential requirements on solving the original PC model are first identified. Then an interior point trust region method for bound constrained minimization is adopted to satisfy these requirements. Besides, the temperature annealing schedule is also redesigned to improve the algorithmic performance. Since the new annealing schedule is linked to the gradient, it is much more flexible and efficient than the original one. Ten benchmark functions are used to test the modified algorithm. Numerical results show that TRPC is superior to the PC algorithm in iteration times, accuracy, and robustness. |
doi_str_mv | 10.1007/s00500-015-1607-7 |
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Numerical results show that TRPC is superior to the PC algorithm in iteration times, accuracy, and robustness.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-015-1607-7</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Annealing ; Artificial Intelligence ; Computational Intelligence ; Control ; Engineering ; Equilibrium ; Game theory ; Iterative methods ; Mathematical Logic and Foundations ; Mechatronics ; Methodologies and Application ; Methods ; Optimization ; Probability distribution ; Robotics ; Robustness (mathematics) ; Schedules ; Trustworthiness ; Variables</subject><ispartof>Soft computing (Berlin, Germany), 2016-04, Vol.20 (4), p.1581-1600</ispartof><rights>Springer-Verlag Berlin Heidelberg 2015</rights><rights>Springer-Verlag Berlin Heidelberg 2015.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-466b440059184697fa9325a580e0db07b621791c8716eb3b197eae40f9acbb163</citedby><cites>FETCH-LOGICAL-c386t-466b440059184697fa9325a580e0db07b621791c8716eb3b197eae40f9acbb163</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-015-1607-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918109457?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Yang, Bo</creatorcontrib><creatorcontrib>Wu, Ruiming</creatorcontrib><title>A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>This article presents a distributed random search optimization method, the trust region probability collectives (TRPC) method, for unconstrained optimization problems without closed forms. 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Numerical results show that TRPC is superior to the PC algorithm in iteration times, accuracy, and robustness.</description><subject>Algorithms</subject><subject>Annealing</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Engineering</subject><subject>Equilibrium</subject><subject>Game theory</subject><subject>Iterative methods</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methodologies and Application</subject><subject>Methods</subject><subject>Optimization</subject><subject>Probability distribution</subject><subject>Robotics</subject><subject>Robustness (mathematics)</subject><subject>Schedules</subject><subject>Trustworthiness</subject><subject>Variables</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UE1LxDAQLaLguvoDvAU8VydtmrTHZfELBC96Dkk77WZpm5qkynrxr5t1BU8ehhlm3nvDe0lySeGaAogbD1AApECLlHIQqThKFpTleSqYqI5_5iwVnOWnyZn3W4CMiiJfJF8rMtjGtAYbMjmrlTa9CTtS277HOph39MROwQzmUwVjR6L6zjoTNgPRykdSXAU3-0Acdvv7gGFjG6LGWGTEDxJwmNCpMDuM2xFVb8aO-HqDzdzjeXLSqt7jxW9fJq93ty_rh_Tp-f5xvXpK67zkIWWca8aix4qWjFeiVVWeFaooAaHRIDSPfipal4Jy1LmmlUCFDNpK1VpTni-Tq4NuNPk2ow9ya2c3xpcyi5oUKlaIiKIHVO2s9w5bOTkzKLeTFOQ-Z3nIWcac5T5nuedkB46P2LFD96f8P-kbwliCBg</recordid><startdate>20160401</startdate><enddate>20160401</enddate><creator>Yang, Bo</creator><creator>Wu, Ruiming</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20160401</creationdate><title>A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule</title><author>Yang, Bo ; 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Through analyzing the framework of the original probability collectives (PC) algorithm, three potential requirements on solving the original PC model are first identified. Then an interior point trust region method for bound constrained minimization is adopted to satisfy these requirements. Besides, the temperature annealing schedule is also redesigned to improve the algorithmic performance. Since the new annealing schedule is linked to the gradient, it is much more flexible and efficient than the original one. Ten benchmark functions are used to test the modified algorithm. Numerical results show that TRPC is superior to the PC algorithm in iteration times, accuracy, and robustness.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-015-1607-7</doi><tpages>20</tpages></addata></record> |
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subjects | Algorithms Annealing Artificial Intelligence Computational Intelligence Control Engineering Equilibrium Game theory Iterative methods Mathematical Logic and Foundations Mechatronics Methodologies and Application Methods Optimization Probability distribution Robotics Robustness (mathematics) Schedules Trustworthiness Variables |
title | A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule |
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