A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency
The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm. To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulati...
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| Veröffentlicht in: | Neural processing letters 2019-04, Vol.49 (2), p.737-759 |
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| container_title | Neural processing letters |
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| creator | Niu, Peifeng Li, Jinbai Chang, Lingfang Zhang, Xianchen Wang, Rongyan Li, Guoqiang |
| description | The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm. To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulation experiments demonstrate that NFPA has better search performance on classical numerical function optimizations compared with other the state-of-the-art optimization methods. In addition, the NFPA is adopted to optimize parameters of fast learning network to build thermal efficiency model of a 330 MW coal-fired boiler and a well-generalized model is obtained. Experimental results show that the tuned fast learning network model by NFPA has better prediction accuracy and generalization ability than other combination models. |
| doi_str_mv | 10.1007/s11063-018-9854-0 |
| format | Article |
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To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulation experiments demonstrate that NFPA has better search performance on classical numerical function optimizations compared with other the state-of-the-art optimization methods. In addition, the NFPA is adopted to optimize parameters of fast learning network to build thermal efficiency model of a 330 MW coal-fired boiler and a well-generalized model is obtained. Experimental results show that the tuned fast learning network model by NFPA has better prediction accuracy and generalization ability than other combination models.</description><identifier>ISSN: 1370-4621</identifier><identifier>EISSN: 1573-773X</identifier><identifier>DOI: 10.1007/s11063-018-9854-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Artificial Intelligence ; Bias ; Boilers ; Coal ; Complex Systems ; Computational Intelligence ; Computer Science ; Efficiency ; Flowers ; Insects ; Learning ; Neural networks ; Optimization ; Parameter estimation ; Pollinators ; Thermodynamic efficiency</subject><ispartof>Neural processing letters, 2019-04, Vol.49 (2), p.737-759</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-7b73f2869e1a47e3d4553db753e170ecbc2a5467c0e4ae0dd6569fbed53ec2ef3</citedby><cites>FETCH-LOGICAL-c316t-7b73f2869e1a47e3d4553db753e170ecbc2a5467c0e4ae0dd6569fbed53ec2ef3</cites><orcidid>0000-0002-4981-5302</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11063-018-9854-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918342623?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Niu, Peifeng</creatorcontrib><creatorcontrib>Li, Jinbai</creatorcontrib><creatorcontrib>Chang, Lingfang</creatorcontrib><creatorcontrib>Zhang, Xianchen</creatorcontrib><creatorcontrib>Wang, Rongyan</creatorcontrib><creatorcontrib>Li, Guoqiang</creatorcontrib><title>A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency</title><title>Neural processing letters</title><addtitle>Neural Process Lett</addtitle><description>The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm. To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulation experiments demonstrate that NFPA has better search performance on classical numerical function optimizations compared with other the state-of-the-art optimization methods. In addition, the NFPA is adopted to optimize parameters of fast learning network to build thermal efficiency model of a 330 MW coal-fired boiler and a well-generalized model is obtained. Experimental results show that the tuned fast learning network model by NFPA has better prediction accuracy and generalization ability than other combination models.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Bias</subject><subject>Boilers</subject><subject>Coal</subject><subject>Complex Systems</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Efficiency</subject><subject>Flowers</subject><subject>Insects</subject><subject>Learning</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Parameter estimation</subject><subject>Pollinators</subject><subject>Thermodynamic efficiency</subject><issn>1370-4621</issn><issn>1573-773X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wFvAczQfu8nusZb6AdV6qOAt7GYn7ZZ0U5Ot0n9vygqePM3APO878CB0zegto1TdRcaoFISygpRFnhF6gkYsV4IoJT5O0y4UJZnk7BxdxLihNKU4HaHFBL_6L3D4wflvCPjNO9d2Vd_6Dk_cyoe2X2-x9QG_-AbSaYX7NeB737pEL9cQtpXDM2tb00JnDpfozFYuwtXvHKP3h9ly-kTmi8fn6WROjGCyJ6pWwvJClsCqTIFosjwXTa1yAUxRMLXhVZ5JZShkFdCmkbksbQ1NAgwHK8boZujdBf-5h9jrjd-HLr3UvGSFyLjkIlFsoEzwMQawehfabRUOmlF99KYHbzp500dvmqYMHzIxsd0Kwl_z_6EfJ81wKw</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Niu, Peifeng</creator><creator>Li, Jinbai</creator><creator>Chang, Lingfang</creator><creator>Zhang, Xianchen</creator><creator>Wang, Rongyan</creator><creator>Li, Guoqiang</creator><general>Springer US</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><scope>PSYQQ</scope><orcidid>https://orcid.org/0000-0002-4981-5302</orcidid></search><sort><creationdate>20190401</creationdate><title>A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency</title><author>Niu, Peifeng ; Li, Jinbai ; Chang, Lingfang ; Zhang, Xianchen ; Wang, Rongyan ; Li, Guoqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-7b73f2869e1a47e3d4553db753e170ecbc2a5467c0e4ae0dd6569fbed53ec2ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Bias</topic><topic>Boilers</topic><topic>Coal</topic><topic>Complex Systems</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Efficiency</topic><topic>Flowers</topic><topic>Insects</topic><topic>Learning</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Parameter estimation</topic><topic>Pollinators</topic><topic>Thermodynamic efficiency</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Niu, Peifeng</creatorcontrib><creatorcontrib>Li, Jinbai</creatorcontrib><creatorcontrib>Chang, Lingfang</creatorcontrib><creatorcontrib>Zhang, Xianchen</creatorcontrib><creatorcontrib>Wang, Rongyan</creatorcontrib><creatorcontrib>Li, Guoqiang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><jtitle>Neural processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Niu, Peifeng</au><au>Li, Jinbai</au><au>Chang, Lingfang</au><au>Zhang, Xianchen</au><au>Wang, Rongyan</au><au>Li, Guoqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency</atitle><jtitle>Neural processing letters</jtitle><stitle>Neural Process Lett</stitle><date>2019-04-01</date><risdate>2019</risdate><volume>49</volume><issue>2</issue><spage>737</spage><epage>759</epage><pages>737-759</pages><issn>1370-4621</issn><eissn>1573-773X</eissn><abstract>The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm. To improve the solution quality and convergence speed of FPA, we proposed a novel flower pollination algorithm (NFPA) which is a hybrid algorithm based on original FPA and wind driven optimization algorithm. Simulation experiments demonstrate that NFPA has better search performance on classical numerical function optimizations compared with other the state-of-the-art optimization methods. In addition, the NFPA is adopted to optimize parameters of fast learning network to build thermal efficiency model of a 330 MW coal-fired boiler and a well-generalized model is obtained. Experimental results show that the tuned fast learning network model by NFPA has better prediction accuracy and generalization ability than other combination models.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11063-018-9854-0</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-4981-5302</orcidid></addata></record> |
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| subjects | Algorithms Artificial Intelligence Bias Boilers Coal Complex Systems Computational Intelligence Computer Science Efficiency Flowers Insects Learning Neural networks Optimization Parameter estimation Pollinators Thermodynamic efficiency |
| title | A Novel Flower Pollination Algorithm for Modeling the Boiler Thermal Efficiency |
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