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001 978-981-15-8546-3
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020 _a9789811585463
_9978-981-15-8546-3
024 7 _a10.1007/978-981-15-8546-3
_2doi
050 4 _aQA402.5-402.6
072 7 _aPBU
_2bicssc
072 7 _aMAT003000
_2bisacsh
072 7 _aPBU
_2thema
082 0 4 _a519.6
_223
100 1 _aJiang, Chao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_947390
245 1 0 _aNonlinear Interval Optimization for Uncertain Problems
_h[electronic resource] /
_cby Chao Jiang, Xu Han, Huichao Xie.
250 _a1st ed. 2021.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2021.
300 _aXII, 284 p. 103 illus., 58 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Tracts in Mechanical Engineering,
_x2195-9870
505 0 _aIntroduction -- Fundamentals of interval number theory -- Mathematical transformation models of nonlinear interval optimization -- Interval optimization based on hybrid optimization algorithms -- Interval optimization based on interval structural analysis -- Interval optimization based on sequential linear programming -- Interval optimization based on surrogate models -- Interval multidisciplinary optimization design -- Interval optimization based on a novel interval possibility degree model -- Interval optimization considering parameter dependences -- Interval multi-objective optimization design -- Interval optimization considering tolerance design -- Interval differential evolution algorithm.
520 _aThis book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming theory perspective, it develops an innovative mathematical transformation model to deal with general nonlinear interval uncertain optimization problems, which is able to equivalently convert complex interval uncertain optimization problems to simple deterministic optimization problems. This model is then used as the basis for various interval uncertain optimization algorithms for engineering applications, which address the low efficiency caused by double-layer nested optimization. Further, the book extends the nonlinear interval optimization theory to design problems associated with multiple optimization objectives, multiple disciplines, and parameter dependence, and establishes the corresponding interval optimization models and solution algorithms. Lastly, it uses the proposed interval uncertain optimization models and methods to deal with practical problems in mechanical engineering and related fields, demonstrating the effectiveness of the models and methods.
650 0 _aMathematical optimization.
_94112
650 0 _aEngineering mathematics.
_93254
650 0 _aEngineering—Data processing.
_931556
650 0 _aAerospace engineering.
_96033
650 0 _aAstronautics.
_947391
650 0 _aEngineering design.
_93802
650 1 4 _aOptimization.
_947392
650 2 4 _aMathematical and Computational Engineering Applications.
_931559
650 2 4 _aAerospace Technology and Astronautics.
_947393
650 2 4 _aEngineering Design.
_93802
700 1 _aHan, Xu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_947394
700 1 _aXie, Huichao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_947395
710 2 _aSpringerLink (Online service)
_947396
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811585456
776 0 8 _iPrinted edition:
_z9789811585470
776 0 8 _iPrinted edition:
_z9789811585487
830 0 _aSpringer Tracts in Mechanical Engineering,
_x2195-9870
_947397
856 4 0 _uhttps://doi.org/10.1007/978-981-15-8546-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78030
_d78030