Branch Strategy and Deletion Strategy Matching on Interval Global Optimization Algorithm
Nowadays, the computing capability is increasing steadily. It has gradually become possible to solve the global optimization problem of complex function. For the global optimization solution, the requirements of correctness and completeness have become more sophisticated. This paper presents a deter...
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| creator | Xuening Fan Yongmei Lei Ronggang Jia |
| description | Nowadays, the computing capability is increasing steadily. It has gradually become possible to solve the global optimization problem of complex function. For the global optimization solution, the requirements of correctness and completeness have become more sophisticated. This paper presents a deterministic algorithm - branch strategy and deletion strategy matching on interval global optimization algorithm (BMD).This algorithm aims at a kind of function who has first-order continuous function in its search domain. This method execute deterministic search in the search range, and can get the calculation results of the deterministic algorithm. Its computational amount is less than the current deterministic algorithm and random algorithms. It can reduce the computational amount and speed up the convergence under the premise of finding the exact global optimal solution without increasing memory consumption. |
| doi_str_mv | 10.1109/CIT.2012.212 |
| format | Conference Proceeding |
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It has gradually become possible to solve the global optimization problem of complex function. For the global optimization solution, the requirements of correctness and completeness have become more sophisticated. This paper presents a deterministic algorithm - branch strategy and deletion strategy matching on interval global optimization algorithm (BMD).This algorithm aims at a kind of function who has first-order continuous function in its search domain. This method execute deterministic search in the search range, and can get the calculation results of the deterministic algorithm. Its computational amount is less than the current deterministic algorithm and random algorithms. It can reduce the computational amount and speed up the convergence under the premise of finding the exact global optimal solution without increasing memory consumption.</description><subject>Acceleration</subject><subject>Algorithm design and analysis</subject><subject>branch strategy</subject><subject>Convergence</subject><subject>deletion strategy</subject><subject>global optimization</subject><subject>interval algorithm</subject><subject>Linear programming</subject><subject>Memory management</subject><subject>Optimization</subject><subject>Search problems</subject><isbn>9781467348737</isbn><isbn>1467348732</isbn><isbn>076954858X</isbn><isbn>9780769548586</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFjE9LwzAcQCMiqLM3b176BVZ_yS9p0uOsbhYmO9jDbiNN0jbSP6MNwvz0DhU8PXjwHiH3FBJKIXvMizJhQFnCKLsgtyDTTHAl1P6SRJlUlKcSuZIor0k0zx8AQAEFSHZD9k-THkwbv4dJB9ecYj3Y-Nl1Lvhx-LdvOpjWD018lsUQ3PSpu3jTjdUZu2Pwvf_SP8Wqa8bJh7a_I1e17mYX_XFByvVLmb8ut7tNka-2S59BWIqqBi0dQ2t5amtnqDQ2taritc4sImpeMRSWIXBmrEypYKgrdJRLYzjDBXn43Xrn3OE4-V5Pp0OKGQOu8Bsj7FKU</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Xuening Fan</creator><creator>Yongmei Lei</creator><creator>Ronggang Jia</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>Branch Strategy and Deletion Strategy Matching on Interval Global Optimization Algorithm</title><author>Xuening Fan ; Yongmei Lei ; Ronggang Jia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-5bf0a7e23dd46dfec17cd6d8b4fa9d333a4b235d23042cd761523ab3e147cc423</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Acceleration</topic><topic>Algorithm design and analysis</topic><topic>branch strategy</topic><topic>Convergence</topic><topic>deletion strategy</topic><topic>global optimization</topic><topic>interval algorithm</topic><topic>Linear programming</topic><topic>Memory management</topic><topic>Optimization</topic><topic>Search problems</topic><toplevel>online_resources</toplevel><creatorcontrib>Xuening Fan</creatorcontrib><creatorcontrib>Yongmei Lei</creatorcontrib><creatorcontrib>Ronggang Jia</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xuening Fan</au><au>Yongmei Lei</au><au>Ronggang Jia</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Branch Strategy and Deletion Strategy Matching on Interval Global Optimization Algorithm</atitle><btitle>2012 IEEE 12th International Conference on Computer and Information Technology</btitle><stitle>cit</stitle><date>2012-10</date><risdate>2012</risdate><spage>1034</spage><epage>1039</epage><pages>1034-1039</pages><isbn>9781467348737</isbn><isbn>1467348732</isbn><eisbn>076954858X</eisbn><eisbn>9780769548586</eisbn><coden>IEEPAD</coden><abstract>Nowadays, the computing capability is increasing steadily. It has gradually become possible to solve the global optimization problem of complex function. For the global optimization solution, the requirements of correctness and completeness have become more sophisticated. This paper presents a deterministic algorithm - branch strategy and deletion strategy matching on interval global optimization algorithm (BMD).This algorithm aims at a kind of function who has first-order continuous function in its search domain. This method execute deterministic search in the search range, and can get the calculation results of the deterministic algorithm. Its computational amount is less than the current deterministic algorithm and random algorithms. It can reduce the computational amount and speed up the convergence under the premise of finding the exact global optimal solution without increasing memory consumption.</abstract><pub>IEEE</pub><doi>10.1109/CIT.2012.212</doi><tpages>6</tpages></addata></record> |
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| subjects | Acceleration Algorithm design and analysis branch strategy Convergence deletion strategy global optimization interval algorithm Linear programming Memory management Optimization Search problems |
| title | Branch Strategy and Deletion Strategy Matching on Interval Global Optimization Algorithm |
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