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008 110707s2011 gw | s |||| 0|eng d
020 _a9783642205361
_9978-3-642-20536-1
040 _cMX-MeUAM
050 4 _aQ342
082 0 4 _a006.3
_223
100 1 _aLi, Lijuan.
_eauthor.
245 1 0 _aGroup Search Optimization for Applications in Structural Design
_h[recurso electrónico] /
_cby Lijuan Li, Feng Liu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aX, 250 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdaptation, Learning, and Optimization,
_x1867-4534 ;
_v9
505 0 _aChapter 1 Introduction of swarm intelligent algorithms -- Chapter 2 Application of particle swarm optimization algorithm to engineering structures -- Chapter 3 Optimum design of structures with heuristic particle swarm optimization algorithm .-Chapter 4 Optimum design of structures with group search optimizer algorithm .-Chapter 5 Improvements and applications of group search optimizer in structural optimal design .-Chapter 6 Optimum design of structures with quick group search optimization algorithm .-Chapter 7 Group search optimizer and its applications on multi-objective structural optimal design .-Chapter 8 Prospecting  swarm intelligent algorithms.
520 _aCivil engineering structures such as buildings, bridges, stadiums, and offshore structures play an import role in our daily life. However, constructing these structures requires lots of budget. Thus, how to cost-efficiently design structures satisfying all required design constraints is an important factor to structural engineers. Traditionally, mathematical gradient-based optimal techniques have been applied to the design of optimal structures. While, many practical engineering optimal problems are very complex and hard to solve by traditional method. In the past few decades, swarm intelligence algorithms, which were inspired by the social behaviour of natural animals such as fish schooling and bird flocking, were developed  because they do not require conventional mathematical assumptions and thus possess better global search abilities than the traditional optimization algorithms and  have attracted more and more attention. These intelligent based algorithms are very suitable for continuous and discrete design variable problems such as ready-made structural members and have been vigorously applied to various structural design problems and obtained good results. This book gathers the authors’ latest research work related with particle swarm optimizer algorithm and group search optimizer algorithm as well as their application to structural optimal design. The readers can understand the full spectrum of the algorithms and apply the algorithms to their own research problems.  
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aMechanical engineering.
650 0 _aCivil engineering.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aCivil Engineering.
650 2 4 _aStructural Mechanics.
700 1 _aLiu, Feng.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642205354
830 0 _aAdaptation, Learning, and Optimization,
_x1867-4534 ;
_v9
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-20536-1
596 _a19
942 _cLIBRO_ELEC
999 _c203979
_d203979